{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from scipy import stats\n",
    "%matplotlib inline\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import Sequential, layers, callbacks\n",
    "from tensorflow.keras.layers import Dense, LSTM, Dropout, GRU, Bidirectional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Datetime</th>\n",
       "      <th>Battery Voltage</th>\n",
       "      <th>Hammer Sensor Hole and System State</th>\n",
       "      <th>Menhole cover status</th>\n",
       "      <th>TEMPERATURE</th>\n",
       "      <th>HUMIDITY</th>\n",
       "      <th>SURFACE</th>\n",
       "      <th>CO</th>\n",
       "      <th>CO2</th>\n",
       "      <th>CH4</th>\n",
       "      <th>O2</th>\n",
       "      <th>H2S</th>\n",
       "      <th>Unnamed: 12</th>\n",
       "      <th>Unnamed: 13</th>\n",
       "      <th>Unnamed: 14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-12-01 17:37:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1131.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-12-01 17:41:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1131.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-12-01 17:42:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1131.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-12-01 17:44:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1131.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-12-01 17:44:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1131.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Datetime  Battery Voltage  Hammer Sensor Hole and System State  \\\n",
       "0 2021-12-01 17:37:00              NaN                                  NaN   \n",
       "1 2021-12-01 17:41:00              NaN                                  NaN   \n",
       "2 2021-12-01 17:42:00              NaN                                  NaN   \n",
       "3 2021-12-01 17:44:00              NaN                                  NaN   \n",
       "4 2021-12-01 17:44:00              NaN                                  NaN   \n",
       "\n",
       "   Menhole cover status  TEMPERATURE  HUMIDITY  SURFACE  CO      CO2  CH4  \\\n",
       "0                   NaN          NaN       NaN      NaN  1.0  1131.0  0.0   \n",
       "1                   NaN          NaN       NaN      NaN  1.0  1131.0  0.0   \n",
       "2                   NaN          NaN       NaN      NaN  1.0  1131.0  0.0   \n",
       "3                   NaN          NaN       NaN      NaN  1.0  1131.0  0.0   \n",
       "4                   NaN          NaN       NaN      NaN  1.0  1131.0  0.0   \n",
       "\n",
       "     O2  H2S  Unnamed: 12  Unnamed: 13  Unnamed: 14  \n",
       "0  19.8  0.0          NaN          NaN          NaN  \n",
       "1  19.8  0.0          NaN          NaN          NaN  \n",
       "2  19.8  0.0          NaN          NaN          NaN  \n",
       "3  19.8  0.0          NaN          NaN          NaN  \n",
       "4  19.8  0.0          NaN          NaN          NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('woojoo.csv',parse_dates = ['Datetime'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CO2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1131.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1131.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1131.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1131.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1131.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62390</th>\n",
       "      <td>1025.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62391</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62392</th>\n",
       "      <td>840.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62393</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62394</th>\n",
       "      <td>800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62395 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          CO2\n",
       "0      1131.0\n",
       "1      1131.0\n",
       "2      1131.0\n",
       "3      1131.0\n",
       "4      1131.0\n",
       "...       ...\n",
       "62390  1025.0\n",
       "62391     NaN\n",
       "62392   840.0\n",
       "62393     NaN\n",
       "62394   800.0\n",
       "\n",
       "[62395 rows x 1 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=df[['CO2']]\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CO2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1131.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1131.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1131.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1131.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1131.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32473</th>\n",
       "      <td>542.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32474</th>\n",
       "      <td>624.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32475</th>\n",
       "      <td>1025.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32476</th>\n",
       "      <td>840.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32477</th>\n",
       "      <td>800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32478 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          CO2\n",
       "0      1131.0\n",
       "1      1131.0\n",
       "2      1131.0\n",
       "3      1131.0\n",
       "4      1131.0\n",
       "...       ...\n",
       "32473   542.0\n",
       "32474   624.0\n",
       "32475  1025.0\n",
       "32476   840.0\n",
       "32477   800.0\n",
       "\n",
       "[32478 rows x 1 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=df1.dropna()\n",
    "df3=df2.reset_index()\n",
    "df4=df3[['CO2']]\n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CO2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00</th>\n",
       "      <td>1131.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:01:00</th>\n",
       "      <td>1131.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:02:00</th>\n",
       "      <td>1131.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:03:00</th>\n",
       "      <td>1131.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:04:00</th>\n",
       "      <td>1131.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-23 13:13:00</th>\n",
       "      <td>542.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-23 13:14:00</th>\n",
       "      <td>624.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-23 13:15:00</th>\n",
       "      <td>1025.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-23 13:16:00</th>\n",
       "      <td>840.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-23 13:17:00</th>\n",
       "      <td>800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32478 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        CO2\n",
       "1970-01-01 00:00:00  1131.0\n",
       "1970-01-01 00:01:00  1131.0\n",
       "1970-01-01 00:02:00  1131.0\n",
       "1970-01-01 00:03:00  1131.0\n",
       "1970-01-01 00:04:00  1131.0\n",
       "...                     ...\n",
       "1970-01-23 13:13:00   542.0\n",
       "1970-01-23 13:14:00   624.0\n",
       "1970-01-23 13:15:00  1025.0\n",
       "1970-01-23 13:16:00   840.0\n",
       "1970-01-23 13:17:00   800.0\n",
       "\n",
       "[32478 rows x 1 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.index = pd.to_datetime(df4.index, unit='m')\n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CO2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00</th>\n",
       "      <td>1131.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:30</th>\n",
       "      <td>1131.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:01:00</th>\n",
       "      <td>1131.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:01:30</th>\n",
       "      <td>1131.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:02:00</th>\n",
       "      <td>1131.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-23 13:15:00</th>\n",
       "      <td>1025.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-23 13:15:30</th>\n",
       "      <td>1025.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-23 13:16:00</th>\n",
       "      <td>840.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-23 13:16:30</th>\n",
       "      <td>840.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-23 13:17:00</th>\n",
       "      <td>800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64955 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        CO2\n",
       "1970-01-01 00:00:00  1131.0\n",
       "1970-01-01 00:00:30  1131.0\n",
       "1970-01-01 00:01:00  1131.0\n",
       "1970-01-01 00:01:30  1131.0\n",
       "1970-01-01 00:02:00  1131.0\n",
       "...                     ...\n",
       "1970-01-23 13:15:00  1025.0\n",
       "1970-01-23 13:15:30  1025.0\n",
       "1970-01-23 13:16:00   840.0\n",
       "1970-01-23 13:16:30   840.0\n",
       "1970-01-23 13:17:00   800.0\n",
       "\n",
       "[64955 rows x 1 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5=df4.resample('30S').ffill()\n",
    "df5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x28ec7b657f0>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# df1['CO2'].interpolate(method='linear', inplace=True)\n",
    "plt.figure(figsize = (15, 6))\n",
    "plt.plot(df5.index, df5['CO2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x28ec8480eb0>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3MAAAFlCAYAAABBWbHVAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAB7KklEQVR4nO3dd5wTdfoH8M8322HpS29LlyZVQGmiNMHu6dn7eXp6lvP0h70rZ72z69k9xd5BEOlF6R3pLGXpdWGBbfn+/shMdpLMTCbJzKTs5/168SKZTDKzyWx2nnm+3+cRUkoQERERERFRcvHEeweIiIiIiIgocgzmiIiIiIiIkhCDOSIiIiIioiTEYI6IiIiIiCgJMZgjIiIiIiJKQgzmiIiIiIiIklB6vHfATF5enszPz4/3bhAREREREcXFokWL9kkp6+s9ltDBXH5+PhYuXBjv3SAiIiIiIooLIcQWo8c4zJKIiIiIiCgJMZgjIiIiIiJKQgzmiIiIiIiIkhCDOSIiIiIioiTEYI6IiIiIiCgJMZgjIiIiIiJKQgzmiIiIiIiIkhCDOSIiIiIioiTEYI6IiIiIiCgJMZgjIiIiIiJKQgzmiIiIiIiIkhCDOSLSVVruxZb9xfHeDSIiIiIywGCOiHQ98O0KDH5uOg4fK4v3rhARERGRDgZzRKRr9oZ9AICjpeVx3hMiIiIi0sNgjoh0iXjvABERERGZYjBHRERERESUhBjMEZEpKWW8d4GIiIiIdDCYIyJdQnCgJREREVEiYzBHRERERESUhBjMERERERERJSEGc0RkilPmiIiIiBITgzki0lV46DgABnNEREREiYrBHBGZ8jKaIyIiIkpIDOaIyFS1zLR47wIRERER6WAwR0SmmJcjIiIiSkxhgzkhRHMhxDQhxGohxCohxB3K8keFEIVCiKXKv1Ga59wnhNgghFgrhBihWT5SWbZBCDHGmR+JiOzEUZZEREREiSndwjrlAO6WUi4WQtQAsEgIMVl57CUp5fPalYUQnQBcCqAzgCYAfhVCtFcefg3AMADbASwQQvwgpVxtxw9CRM6QzM0RERERJaSwmTkp5U4p5WLl9hEAfwBoavKU8wB8JqUskVJuBrABQB/l3wYp5SYpZSmAz5R1iSiBbTtwPN67QEREREQ6IpozJ4TIB9ADwDxl0W1CiOVCiPeEEHWUZU0BbNM8bbuyzGg5ESWwdbuPxHsXiIiIiEiH5WBOCJEL4GsAd0opiwC8AaANgO4AdgJ4wY4dEkLcJIRYKIRYuHfvXjtekohi0L157XjvAhERERHpsBTMCSEy4AvkPpFSfgMAUsrdUsoKKaUXwH/hG0YJAIUAmmue3kxZZrQ8gJTybSllbyll7/r160f68xCRTdI9It67QEREREQmrFSzFADeBfCHlPJFzfLGmtUuALBSuf0DgEuFEFlCiFYA2gGYD2ABgHZCiFZCiEz4iqT8YM+PQUREREREVLVYqWbZH8BVAFYIIZYqy+4HcJkQojt8bagKAPwVAKSUq4QQXwBYDV8lzFullBUAIIS4DcAkAGkA3pNSrrLtJyEiR3jZm4CIiIgoIYUN5qSUswHojbeaYPKcpwA8pbN8gtnziCjxMJYjIiIiSkwRVbMkoqqHmTkiIiKixMRgjohMMZQjIiIiSkwM5ojIlGRmjoiIiCghMZgjIlOM5YiIiIgSE4M5ItKlxnBeBnNERERECYnBHBGZ4jBLIiIiosTEYI6ITDEzR0RERJSYGMwRka4KJYqTrGdJRERElJAYzBGRKY6yJCIiIkpMDOaIyBSDOSIiIqLExGCOiEx5Gc0RERERJSQGc0RkiqEcERERUWJiMEdEpgoPHo/3LhARERGRDgZzRGTqsR9XxXsXiIiIiEgHgzkiMsVhlkRERESJicEcEZljNEdERESUkBjMEZEpNg0nIiIiSkwM5ojIVK2czHjvAhERERHpYDBHRKbuHt4+3rtARERERDoYzBGRqax0fk0QERERJSKepRGRKckpc0REREQJicEcEZliLEdERESUmBjMEZEpydQcERERUUJiMEdERERERJSEGMwRkSnm5YiIiIgSE4M5IjLHaI6IiIgoITGYIyJTktEcERERUUJiMEdEprYfPB7vXSAiIiIiHQzmiMjUK1M3xHsXiIiIiEgHgzkiIiIiIqIkxGCOiIiIiIgoCTGYI1PHSytQXuGN924QEREREVEQBnNkquPDE3HdBwvivRtERERERBSEwRwZktJXkn7W+n1x3hNym/rZExEREVHiYjBHhliSvupiLEdERESU+BjMEVEIL6M5IiIiooTHYI6IQjCUIyIiIkp8DOaIKAQTc0RERESJj8EcEYXgMEsiIiKixMdgjoiIiIiIKAkxmCNDFV5mZ6oqJuaIiIiIEh+DOTJUwTP6KovDLCmVlJRX4MVf1uJ4aUW8d4WIiMhWDObIkJeZuSqLnzylkk9+34qXp27AGzM2xntXiIiIbMVgjgwxlqu6Vmw/HO9dILJNSblX+Z+ZOSIiSi0M5sjQzHV7470LFCeX/ff3eO8CEREREYXBYI4M/bZpf7x3gYiIiIiIDDCYI0NlFd547wIRERERERkIG8wJIZoLIaYJIVYLIVYJIe5QltcVQkwWQqxX/q+jLBdCiJeFEBuEEMuFED01r3WNsv56IcQ1zv1YZAe2JiDV+t1H4r0LRERERBTESmauHMDdUspOAPoBuFUI0QnAGABTpJTtAExR7gPAWQDaKf9uAvAG4Av+ADwCoC+APgAeUQNASkztGuTGexcoQQx7aWa8d4GIiIiIgoQN5qSUO6WUi5XbRwD8AaApgPMAfKis9iGA85Xb5wH4SPr8DqC2EKIxgBEAJkspD0gpDwKYDGCknT8M2evbJYXx3gVKIGxVQURERJRYIpozJ4TIB9ADwDwADaWUO5WHdgFoqNxuCmCb5mnblWVGyylBFZ0oj/cuUAK59oMF8d4FIiIiItKwHMwJIXIBfA3gTillkfYxKaWETX2GhRA3CSEWCiEW7t3L0vhEiYKtKijpMblMREQpxlIwJ4TIgC+Q+0RK+Y2yeLcyfBLK/3uU5YUAmmue3kxZZrQ8gJTybSllbyll7/r160fysxAREYUQIt57QERE5Awr1SwFgHcB/CGlfFHz0A8A1IqU1wD4XrP8aqWqZT8Ah5XhmJMADBdC1FEKnwxXlhERETlGMiNHREQpKt3COv0BXAVghRBiqbLsfgBjAXwhhLgBwBYAlyiPTQAwCsAGAMcAXAcAUsoDQognAKgTbx6XUh6w44cgIiIKixk6IiJKMWGDOSnlbBj/CTxTZ30J4FaD13oPwHuR7CAlrqvfm4+zuzbGJac0D78yERERERHZKqJqllR1Sc04pWOl5Tj/tTmYuW4v7v16eRz3ioiSxfLth3DoWGm8d4OIiCilMJgjSyo0PcYWbTmIpdsOxW9niCjpnPvqHFz69u/x3g0iIqKUwmCOLCnXBHMsJkBE0Viz60i8d4GIiCilMJgjSw4dK/Pf/vj3LXHcE3Kadihc67zqcdwTIiIiIjLDYI50yaD0W2Z65aEyefXugMfKKryu7BO5Y39xZTD3xc2nxnFPiGzGUQVERJRiGMyRLm/QSU9wcBe4Ls+QUklJWWVwnpebFcc9IbIHm4YTEVGqYjBHuiqCorng4E5rw56jDu8Nuam4tNzwsdU7ilzcEyIiIiIyw2COdJV7A4dOmmXmDh8vM3yMks/mfcWGj70ze5OLe0JEREREZhjMka5jpRUB980yc09P+MPhvSE3fbekMN67QEREREQWMJgjXf3HTg24L00qB6ws5NC7VFIcFMhrFR03HoIZq4J9xcgfMx5Lth50bBtUNXFaLxERpSoGc6SrpDxwmKVZZo5SS8dGNQLut22Q6789b9N+x7Y7Y91eAMC3zAymHO0w7a37j8VvR1gIhYiIUgyDOdI1pEP9gPtek2iOFQ9TS6nSaqJp7RwAQCtNr7kjJeWm8ydjoVYcZHXU1Lb9UByDOSIiohTDYI50rY+gQuW+oyUO7gm5Tc3KZmXofz1EcmxEQijRHGO51MPPlIiIyBkM5kjX9oPHA+6r2ZLhL82Ix+6Qi0rVYC49TfdxbR86O3n8mTlHXp7iSPuRlpY7c/xYwmOLiIhSDIM5MtW7ZR0AlSfY63aHZmUu6d3MzV0ih/kzc+m+r4fgaUZONWAWUDNzPONOZfHoS8mm4URElKoYzJGp5nWrATA/wT6pUU23dodcUFLmq2aZme7u14OamWMsl3q03x9t6uearElERESRYDCXJMorvNh+0L3CATcOaAUAOCW/LgDzoW8sWJFagjNzwZ+uU1kOj/LC5V6JE2XG7REo+ZzQDK00a3NCREREkWEwlyTOe20OBvxrGvYecafYSF4NX4XK9LTwQ98qOMkppZQEzZkrDJo/KZyq76687NeLt+OkhyairCKOc6vIVo/9sMp/m9d+iIiI7MNgLkms2uFrzH3wWKkr21NPuNRsidn5VwXPzlJKabkvK6ZWs1y9052m8J6glB8vEqSOHYcrLwjw64KIiMg+DOaSjFsnQupQqDTlCPFKiaITZbrrmvWgo+SjZn8z0/S/HpwaVusJSvjd8dkSR7ZD8cVvCyIiIvswmEsybs03Cc7Meb1AmUFJ8UmrdruyT+SO289sBwC4qKd+ldLJq535vIPn4vG4Sh3a+J/VSomIiOzDYC7JeF2eRuQP5qQ0HPa2ovCwm7tEDmtYM1v5P0v3cac+7+BhlpSa4hHKMX4kIqJUxWAuyZS6VBRCvXqephn7xrlxVYP6KQuD4OrCnk0d2a7R9ij5BWbm4rcfREREqYbBXBLYvK/Yf9utIUqlFb7tTFuzBwAwd+M+zFq3z5VtU3ypx1hwbHVe9yYAgFo5GY5s98BRdyq1Ury5H83xOgEREaUqBnNJ4KHvVvpvu9XT7eUp6wEAx0p9lQ2Ljpfjge9WuLJtii/1EAs+/52zwRfMvzJlgyPbrZerP6yTkp92ri8zc86avX4f8seMx6odHP5ORFQVMJhLArM3VGbEMtPSXN32FX1bAABOa1MPZRU8C6sK1BNvdQ7b8E4NAQD7jvraYmw5UKz/xBg1q5PjyOtSYuG3iLMmr94FAFiw+UCc94SIiNzAYC7JuDVcqJFSBCMj3XeIlLP9QJWhFtlRj7VfgqpX7i7icEiKjPaYOXRMv8UJ2Yvf2EREVQODuSTjVlA1rFND1K2e6S+Aoq1kWSM7PWDdvq3qurJPBBwtKXd8G/4CKCEDLd3ZLqUe7bzfbQePxXFPUh8LCRERVS0M5hJccENuo/YAtm9XSngEcLDYN7Tu9emV86Sa1g4cDlfmUoXNqm7DniPo8sgkfLFgm6PbMSqAQmSHN6ZvjPcuEBERpQwGcwmu6ETgkKRjpc5nZgDAFzMK7FMqDC4oOOh/LCs98LDZfvC4K/tU1d3x2VIAwFSlwqhT/AVQEiCY27DnSMiyiSt3Yfn2Q+7vDFESYaEZIqKqgcFcglu1oyjg/lXvzsfcDW60CPBl5jLTQw+R4GXVs9JD1iH7qceC00GWWgAlEYZrDX1xZsiym/+3COe+OicOe0NERESUWBjMJTi98tILtxzUWdNeXq+vmqFe9cyMtMrDRojQEvbkrBWFzpYcV6/oq/3i8+tVAwCc3KyWo9tVDWyX58p2qOpxMlm1p+gESssTZ8i524k5KSXem705ZDQJERE5i8FcAjh0rBQz1u3VfezpCWtClr04eZ3Tu+SfM6eXmfvzKc39t6UENu1zplQ9Aa9N24CvF20PWOb0sFavv8+cL5qbdNcgXNWvJb68+VRHt6u6fkArV7ZDZJcKr0Sfp6fgri+WxntX4jY8eu7G/Xj8p9V45PtV8dkBIqIqiuPjEsCNHy7Ewi0HsfzR4aiZnRHv3QHgO6EXQoTMjwOAetXZ3Nktz01aCwC4qFcz17ZZ2WfOdz8rPQ1PnN/Fte0z00tOcerY8irp7Ikrdzm0hcR3vLQCAFB0nJk5IiI3MZhLABv2HgUAlCdQU24pJYQAOjauGfJY3eqZcdgjcou3sjdBiMx0D1rnVXd1f4gSHS9AVAa0CTDVloioSuEwywSQiH/7JHxz5urXCM3Cta7Pk/mUpp6U6RyZpeVerNkVWmGSiCrbelRFlT95Iv5FIyJKXQzmksSfezcPv5KN1Dlzejw6l16r8klMqlE/SaPPnygWtaslxlDyVOf2d3IitTQhIqpKGMwlkJ2HjQtbtFAqCrrFK/WDNkD/JJ+xXOpQG9UnQmsCSj0nNaoR711IaXoZdXeoGX0iInITg7kEcPCYb8L43z5ZHLBc2yC8bYNcV/ZlZeFhLNpyEF5lzpwebZDXrE4OAKCC0VzKMJky55ouTUPnalJq8Dr8VXHyo5Pw35mbnN2IAad/tkTm9bc0YThHROQmBnMJ5OiJ8oD7N3ywEADQuFY2mtdxJzN39iuzcdEbc5UCKPp/lLWL05Q0nZfBXMqQCXBS1qEhg7lU5XU44ik6UY6nJvyh+1hV+JaK168th1kSEcUHg7kEEhwQ/bZpv/92pyY18cplPVzbFymN50xpg7xMpYG4N3F65VKM/MdhHE/KjrDxcMqKx4UfNw/l4pLy8Cu5wO23mUEcEVF8MJhLAjsPnwAAnNOtiWvb9BVAMf7r3KGhb97L+j2+tgqrdhx2Zb/IeSXlvsg8nidnWRlp8ds4OSoVhyJqf6QP5hbEazcAxH/OGgdpEBG5i8Fcgik6UYb8MePx2fytuo+f260JWrnQ52vRloOmBTDUuXKqfUdLnd6lKq99Q3fmTaqNyuN5UsZhu6lrk9JXM1XFu7Lvoq0H47LdeAeRRERVFZuGJxAhBLYdOAYAGDtxjcE67pzo7jtaitJy47GTNbIDD514n8CkOiklamS7W9Ld6blNZng8pa48nd6VZJ8lWw/FdfuySsxMJCJKHMzMJZjfNvrmyR06pj9nyCOEaxmTIyZzPx47rwseOaeT/34qDp1KJOVe6Q/iB7bLc3RbtXJ8QWO1rNChjmqydsch4zYaduAczNRSLbPyWIpHnM6vJ+dxzhwRUXwwmEsgB4pLUR4mKhJwbwia3pw5dXhlrZwMXNe/FZrWzlHWdWWXqqyi42X+gLnC4cj58HHfhYSs9NBgTj30CvYV275d7WHNYZappZ3SWiUnIw3lcYzUnfqaSsTDNV4ZskR8L4iIUhmDuQQT7kRdOJSZKy4p95/EB+/Ln3o18y/r3yYwK6T2A9t+0NlMTVXX68lf/cMenQ7mrChzcB+EEK5messqvHjyp9U4WMx5n06pWz0TXZvWwuiTGzPrSkREZKOwwZwQ4j0hxB4hxErNskeFEIVCiKXKv1Gax+4TQmwQQqwVQozQLB+pLNsghBhj/4+SGnLCVPETwpn5RJ0fmYRuj/2i+9jQjg39t9PSAq9td21aCwBQ6PCwO6rMViVCMFdeEfsZ+bmvzkb+mPG6j7k5Z+6VqRvwzuzNePTHVa5tsyoSAkgTIq6ZOTckSmYqUfaDiIicZSUz9wGAkTrLX5JSdlf+TQAAIUQnAJcC6Kw853UhRJoQIg3AawDOAtAJwGXKuhTkpEY1Av4P5hHxnf+RETSesnMTXzCXX8+dpuZVmRrELdwSn2p1WmU2BHPLtxu3szijYwMAQMOazhfLeHnKegDApr32Dx0lH/U76/OF27C7qATrdh+J636QEzjWnogoHsIGc1LKmQAOWHy98wB8JqUskVJuBrABQB/l3wYp5SYpZSmAz5R1KYiadKlbPVP3cQER1/lEaR79Q+aLhdtd3pOqx6goTjy8MWOTo69/eZ8WqJGVjt1FJdhTdMLRbalWFLJXolOkDDzVH/7STFe373SYoZ2fliiFQOL1V4IBMxGRu2KZM3ebEGK5MgyzjrKsKYBtmnW2K8uMlhOA2tUqS85XKIFamkFFEY8nvsNn0oOGWe5WTrRX7yyKx+5UKbtcCmrMqIflsm2HHN2OEMJfTfXH5Tsd3RY5TwKAEGhQBdoSHChOnIsubkqUIJaIqKqJNph7A0AbAN0B7ATwgl07JIS4SQixUAixcO/evXa9bEI7++TG/ttq1s0jBDLS9P46ulscAgj8Ix0cZHpYxrJKicd0PfacS35SSggAe46UxHtXHPfenM3x3oW44q8rEZG7ogrmpJS7pZQVUkovgP/CN4wSAAoBNNes2kxZZrRc77XfllL2llL2rl+/fjS7l3S0f/wqKnx39heXoKwi9K+iL3Zy569l5yY1Q5alBwVvabwcSw7jyWFq4FdFauPHS0QUH1EFc0KIxpq7FwBQK13+AOBSIUSWEKIVgHYA5gNYAKCdEKKVECITviIpP0S/26lFe66qDrOslpGuu64Q7mVHggM337LAQ8ZgCh05bN/R+GQ4RnVtZPtreuNYnXN3AgxdrQqC58xRKuPVFyIiN+lHDBpCiHEATgeQJ4TYDuARAKcLIbrD961dAOCvACClXCWE+ALAagDlAG6VUlYor3MbgEkA0gC8J6VkHXCFdhjZ3A37fDcMznw8Qrg27Exv3l7wnLmuTWu7si8UaPGWgxje2f7A6v5vV5g+HhzMR0vbXqFCSnhMTvWdbH78+6b9jr02VZKQECmcmkvE7HEi7hMREdkvbDAnpbxMZ/G7Jus/BeApneUTAEyIaO+qCG3bpQ9/26Is0/9LLGB/Zq5YKTQRsi2dk6/gAK9tg1x7d4Ys8Th0YvzpvK0AjPsdtqlvz+c95uvl/tsVXgmz9opOnpRqC/c8c2FX5zZEzMwRERE5gIPkEoBe5iHb4OxWaDJzR0vKsd+G4XZGlSj1Tr70hl6Sc4yqmjrteFmF7vILe9pThFZbmTOeGYS3NC0WeGw7x63P2GjUQlVMUjmZ0daTyplXIqJEFjYzR87Ty7Rt3lfZwPjXfwz23xai8sSoyyOTAAAFY0fHtP2PlWxgsHJlx7R/onnC6x6vVwYMR9Ry4rzpsIU+ds3r2tMcXnviVxHmTN+tU9KqeMLvFilZAKWq4PBOIiJ3MTOXAPSagGszF9qhjB4hQk46S8r1syhWHTxWqrtcL3BLS+Mh45ZwQY7dvly0LfxKNjleWjm01yhgVe067EyRkq37jwUu4EmoYyQkBATuGdEhLtuvinGk20FVVXyPiYgSAc/ME4HOH13DjAxCg79fV++JafOz1u+zvC4zc+4xOxkbv8L+RtpunvwtKDjov33I4GKC6oO5BY7sw5YDxQH33R6WVpX8vukA5hccwN9Ob+PodpgVij9+BERE7mIwlwD0MnOqmwcHnvx4PCLkhMWpinx6w6LiNYerKjILLlbv0J/nGAurmcABbfNQI8u+EdrawM5NpeXegPsMBJzHeVWpix8tEVF8MJhLAGajzHq1rBNwXy8z9/Hv+nPerDKqXCh0Bs5kpPEvtlvMgosGNbNt3dbqHUU4Vlo5XDe/nvHcuOZ1qyE706T8ZIROGBRbcdq3SwoD7jOWc8ffz2jr2Ik/P8NKm/YWh1/JAW61ziEiIh8GcwnA7E9f8LBGocyZu/b9+bZt3zAjIyq3qUpjl3DXuHVOtGZXEUa9PAsvT1nvX1YQPJ9MIzNNoKzCa/h4pN6bs9m214pE0YnAlhw8B3WHV0pICawsPBzvXbFNIh47+4tjr3RMVNXN3bgPz09aG+/dIDLFM/MEoDfMsnndHAC+YZVavmqWEtPX7rVt+/3b1NNdztYE8eXWHK7dRZGd9GWkeVBWHlswV696pv925THl7hlxcAaBc+bc8dq0jQCAs1+ZHbb4DUWP39REsbv8v/Pw6rQN8d4NIlMM5hKA3rCUWjkZAIC0oPFIHmH/VWCjLAznzMWX2XluXm6m8YMOy0j3oKwitoPwxoGt/bcPHw9sicAjrOowmy8cDQ7xix/OmSMiig8GcwlA7/xDXRY8qlFA2H4CpO1pp5WblRGyjJk595idmDatnePingTKSPOgtMIb04mz9hi2mhksLfdi7gbrlVfDGdW1ccB9xgHu43ueeg5Y6FdJRET2YTCXAPSCMzUrkx4UzXmEe4PRcnSKXKSzz5xr1M9ZLxv6ylRnh30sfXiY4WOZShGc8hiGyGmH19Wtbi3LOPbnNbj8nXlRbzPYfd+sAABc2KMpABbPiAe7h7aGezVm7pyjFsxatu1QfHeEiKiK4Zl5AtA7Jy5RKvyFxE4itDWBU0TQ/4B5Zo4nSvZS387gobZ2C3716f88HbWrGQdYGcpBGUsRFG0gGNwiwMgPy3ZEvT0z/dvmAQCWbI1Pi4SqrGCfcaEdOzk9BDAR51uyDQQRUdXAYC4B6MVAm5Shj1uC5rO5Ocox0jlzLGZgLzU4vqxPc/+yt6/qZf92wtwP5g/mymPJzFUGcFaDOe1z7HSs1FfV8pvFhWHWJDu0b5jrvz3i3zNd2abb15l2F51wd4NERFRlMZhLAGYZraMlgeXT9Xq/xerKfi0AGPeb09LLzDWp5et5FmtRDAqkHhb5edXRJ78uAGcK0AQffw1qZJmuv6DgAABg3Z4jUW9z5rrKuW+lFjN8TmUajpbEp89dVRF8fLVrUMPBbZk/7la26sLX57qynUSSiNlJIqKqgMFcAjAraBJ88n7Agd5B6rl0cL853dYEOnPmzu7WBID1k3KyRv00PJoT0Kx0+5p168lM96B6VrrpOj+v3AUAmBNDMZIVmh5jfzu9TdSvY4c+rerGdfup7kjQBanhnRvGaU/cU3joeLx3wfWqsGrGvka2+fcHERHZi8FcAjC7nhk8X6qxA1UMvcrwSK+FYZJ6mbm3Z24CwInvdlODfCGAa07LBwB0bOxcVgOwNj8vM93er428XPNMIOAbimnnnEx1aCXACq1OU4+pM09qAAA4r3tTV7Zb7uLFpUScLuz2lDn1PWjf0NnvKCIiCsRgLgGYxVDBmTknzjunrt0DQCczp5wNaE8K9Ib5natk5qyclJN16schAIw+uTEKxo5GPQfeY+2nbmUYp1r90WPT2eKh4+FLmd/71TIcL7NvOGRJWeWJfrM6lRdIWMTHfuo7emqbei5sq/LzGzd/q+PbSzQnNL8jwf0b3cLfISIidzGYSwBmf/yCsyB2nUBrHS+tUPYjcLnuMEudk/0hJ9UHAGw94E5luqpg5+Hj+HnlTt+doM+8QY2sgKIoMdN87lYuFhTs9xXnmb0++mGW3ZrX9t9+ecr6sOt/t3QHTpTZl2nRHura9h+s4WO/ygyzu6mi4tKqNxdy75HKYfjhhkvbjb86RETxwcHtCcDsQma/1oFXs50ogBFcZMWM3py5CSt8c6hu/t8iFIwdbdt+VWWnPjPVfzv4I/cI4VjlUCsn3P5qljFUlyw8GN/AP2CequZHLvd6keZxdl5iVSOVw8SN0azaj7UqJoi0xzUvTBARVQ3MzCUAswIowb28jDJzjgxt0dmUXmZu456j9m+7ith7pAQXvD4HG/cav4fBFUzTPMLWEzXt0LTjFrIZDWr4qpdWz4z+WtC+o6VRP1eVP2Z81M/dU1SZwdD+SrEiq/38mTnNsvx61fy3V2qK4dhJr7ri2zM3xVS4x3hbiUF7kcft4Y4cXhl/d32+FKP+Myveu0FELmMwlwDMgrngTIknKJi6bUhbANH3eDsR4TwkvcxgloWWBqRv4qpdWLL1ED6cW2C4TnD8LoT5MROLjLTw6ZNblOqT5/dwp5CFE0a9XHnCo/2J3SyaUVWox6r2u+vhczr5b5dY7DMYqWcnrvXf1v4OXfHOPEe2p+fLhdtcnbvmreKZyaru2yWFWL2zKN67QUQuYzCXAMz+6AZXFzxUHJjRqJblC6TKowzmHvxupeFjakZIuwt6mbn+LhQ2SFXq1Wyz4Cz4Ld9+8Djmbz5g4z5U3rYyzygn03fMpUpjZO0FE6sNzMk69fASBi02th4odnmP3LFqx2Hc89VydHvsF7w3e7Mr25QBwyxdzsy5ujUiIlIxmEsAZn9zg7MyL0xeF3BfDa6Ch2Na9dWi7Za3Dehn5i45xcZiHFWMGpTtNxl2qNcofvvB+PWxylaK8kxatStu+2CngMwcJxrZzp+Z07zR2u+Wuz5f5vIeuaNY04z+8Z9Wu7JNbUVip+bVhsPfICIidzGYSwCxXEFVK/E58YfbatPwbIcbWaeyn5b7KlaaZtocLhwR6eGnHnPLt0c/1yndI1C7WkbUz7eTNrD46Lct8duRFKUeX9r5vv1aOZPNNzqW4zHkcOJK9y92aP8ONKtTzWRNByib3rS3mPPniIhcxGAuAUgAnZvUjPh5f+rVzD/Hya3CDXrDLLV9uig6tXKMAxsn2lFoLdt+KKL10yzMqwun3Ctx6Fjsc4linY/UOq96QObzzRkbY90lCqKXmQue+5vs9IKX9+a4M7RSS1tgtn/b+Ax/P3y8DJ9WwR5/RETxwmAuAXilRN3qmRjUvn5Ez+vevLY/U1YeQ5l4I1aHWabaiZmbLu/bIuB/PU6/u29MjyyA0Qvo7RDNxfxXp4bvUWfmjJMaxPR8Ck9NFukNFwaAU/LrOLLdprWNLzLNdaCiZTgF+5yfG6gd5ZHjcmEqbfXQ5ducqVBKREShGMwlAK/0FQfQO9Wpn5sVcP+D607x3560apf/xLrcwcycds+cOpGvqtQCNxk6w1dVTvda1s6z6d0y/Im1E70OtSL5eWMtPtmgZpbj729V5/WqTcP1Hz/jpIa2bUsbUFx1akvD9S53saKlavN+54M57e9yPAc66rWFICIiZzCYSwRSwiNCT3aqZ6aFZL3a1M/13z7n5CaVDZwdKKmuF16GO5F3Yu7esBdnIH/M+IjbKCSDCp2y7cGcDja0GTErWVYnAvpoe8bFWrHvgh7NYno+hac3Zw4AWuVVBwD8a+IaR7frtvO7N0GTWtkhy50eLg0AR0+U+2871b/PSFVv2E5EFC8M5hKAV/r+0Af/qdc7sdaevPbSDE9atSO63jJ9WtU1fEzv3CO4712wl6fENuxNz3qlKfniLQdtf+14U7MWZvGRGyeBldsKv064YyAcN/tuhVO/RhYyTbKiFDs1S+MJeptfvbyH/dvSBhQuZoe017CKSyuw43Bo2w43BjVoL3gdSqDfMyIicg7PYhKAV0oI6DQI1zlp1haNaFo7B2t3HwEA/OOLpVFt285+ZQDwyTznqgEWnUi9kxM1kxmc7WpeNz5FZSINHKOpWnf1u9aGuHVpGr4o0C6dk+ZIcc6ns7wGmbksh6vgag9NvcPazp6C6siIW4e0weTVu3XXMZoz6JTWSubTLdr3+8tF23HcQs9KIiKKHYO5BCAN5szpnWOmayoJeoTwz7lyYnhjNHOjnMwipeLQHaMT3U6NKwOZDUpm0g2RfuZHS8rDrxRkmcWWBl/89dSw6xSXRr59r87vStsGlcOXtx04FvFrkjF1NEHwxSqnE856n7PWChuHIarfTY1qGV+EOeLCxSjt90i8vy8vfmtufHeAiKiKYDCXALwGc+b0Tqy1FdqEqJxz5UR/2BrZkfcBy3fwanBFvM9OHGB0oqv9PN+auSngsQFt8xzbn0iHUL7wy7rwK0WpWmZ62HWi6XXX+v4JIctKyiuzCPd/uyLi1yRjavY2+MhqWdf+Pmjab4hw34nLth2yb7s67ReCPfrjKtu2Z0Tb9D7SliOxCn67VxZGN/SfiIgiw2AuAfgyc3pXrkPPDGoGBViTHGxMe8eZ7ZQdieBJDsZbTmQf4+myt3/Ht0sKAYQOV9TerxPUXHu2Ulb91k8XY/Z6e0usR9pC7tN58e0nZdf8u6v75ftvz1q/L+WOtXgyyj6na+YqOtFkOtycud1FsQ/RVWl/xltOb2OwvRLbtmdEe9z+tHyn49sLh83DiYicx2AuAUhI3QIoaTrBnHZ+T5oQuF0JuOpWz7R1n3q1rIOcTOtzWtRs0b6jzp2wpNoJ9m+b9vtvB5/zaH/UZnX0Mxjjl+/ElRbnn1kV6TDZ0hiqqJ7XvUnUz9WKtpJrXm7l78yFPZva8poUSq9peLDtB487sF3zx+1s6q39GWvlRD6iwS7Boxfc/M7UC9wYyxEROY/BXALwV7MMOtnZFebKsccjMFhpNH5xL3tLrEc6nUUNMjc52Bh37sb94VdKUsFZBG3V0luH6F/pd0KslSojcUq+cSVV1cB24YeUfjCnwPI2tUUZerSorAZbL6ifY3mKXTiIJ68SF7txbGkDinBZoTIbe3Nqh0sHFzN67fKeAIDnL+5m2/aMVHgDL0JEM6fUTrG2DiEiovAYzEVo6/5jeGnyOluq6Km8UgIiumpnamYkeF5VrCKdn+ZGQcCvFm13fiNxEhw7aO9nB1X9O6lRDcf2w2qVfr0+WpHKTA+/sUUW2lFEUuV028HK4iZmx+ySranXBiNe/K0JTN5v7ZxF27brYt8zbS+94LnObRr45hFXi2CkQ7TKgwLUcEVg7KS3pVSc50xElGgYzEVoy4Fi/GfKev9cJ1sYZOYa23DCHK0lWw9FtP4ZJzVwZkeqiOBznoCsgosVza0Os4zlHFGt1NmzRW0M6VDfdN0MC9FlJG9PSVll5sLsPPOqd+enZCuMeDBqGq419MWZ9mxLc9vNrJB2mOWEFYFz1ZysOBwseBvxHpru5WhlIiLHMZiL0GltfMO+7JxTY1TN0srcCzdGxVnZRL/W9Rzfj1Yu901yU/CJp/Z+TkbgFX0nz1GtnvzFMgyxUxNfMNe2QY2wrRB+vmNg+BeM4JfgwLFS/+05G8yLx2x0sSVEKqscghj6WEakFXci2q5jL224LSGABQWBWV11CLobwWVwJszNtiZ6P56bjduJiKoqBnMRUs89j9nYENXrG2UZMszSSpZE+5xfVu2K2/CwE2WV74dTzWLV+YGp6MHvVqLro5P8971e33DKpy7ogu7Nawes6+RJ4S8GDY+DnR4mo2ZGO1z21z/2mK7bpHb45umRVMzbd6SyQE9xmOP0+g8WRNVHjwIZVbME7J23Fqy8wout+33Dap2+6FXZmkBgkOZ76r1re/t/bleCuaAI9pZPFju+TTOcekpE5DwGcxFSJ/G/OWOjba+pVrMMToF5DD6dx87tjCfO7wIAyM6oXOmmjxfhgtfj06hV23T5k3lbHNnGB3MLHHndeNALeI+cKPcv90qJWjkZuKJvS50edMZtDGLVp1X4oiQAcOfQdrZtM1aRXDy4+8tlltc9eKwMt/xvUTS7RBpmmTktNfCKhfZX4Z3ZmzHouWnYftD5JvCVmTmBmwe39i+vlZOpGWbp+G6EzJk7UFxqsKYT9KpZMpojInIag7kE4PX6TgKstCYAgGtOy8dV/VoCiK6xtxO0DZ6dvNqeKj7+vUB3uTosSkrjzOylp7QIuG/n+VKretaGsjasWTmfM5ITNidO7pysPLmg4IBjr11VaLNWwc7pVtmeYtBz0xzZvhvNq7VNw/dqsr+ZaR7/RTk3ipHEe45csATbHUohZzw/HVe883u8d4MoITCYi8GeI/ZUtJRSQojQ65pulom3k5UKhFYFnwC5WZ3NSdpCHFq/K73nvFIaZmYHBQ03tTMTur/YWp9AbWGScC00tJy4UF/uYJWFEwafE1lnNsyyYY2skGWxWKwzzPydWcaVfu26uKD9GYtLKjPFXZrW9M8LXbPriC3bMhPPlhq6c+aYmSOHbNpXjDkb3GtXxGOZEhmDuRjYNblcwndFd/zyoCpobtT7N9BRqTgIRB5U/vqHtXlXVgQPKVy6/ZBtrx1PRid2aZpiCUaZuZo56QH3H/p+VdT78cfOwKxFVnrk5dOfm7TW8rpOzBtyevjaysLDzm4gxVVWswy/bnGMcxSve39B6PZN1t+4157vcHXOsFdK5GZX/n4KIVBa7jtA7WxSbiS4zxwAHDrmzlDLUp1fxBS59kZElNAYzMXg8v/Os+V1jE7c4xjLoUZWeviVHLLj0HHsUbI9wScDqXJxbHxQ+XKV2nvNK42D6Ma1whcFseofXwTOIYsmGbx6h/VhbAX7jZvKW/lo9Sq8ntrG2Uqqdp3wV1X+AF7n2LqgZ9OA+3/5aKHt21+05SD+9/tW3cfsyrze85Xv9+irRdvRqXFgH8ho+odGS+/Cxo0f2v+e6rnjs6Uhy9g0nFIFD2VKZAzmEoDvxD10udWeX3b42+ltAu7Pj+NcodPGTkWfp6cASOyTASml7UMv6lXP9L+2G8G83pX8SEUyfCy4bLtW5Tl/4A/etWkt/+2zujQKeV6079Mp+XV0l1/Wp3nAfb2TVLLOazJnLjgTPHfjfrw32/4M1tYD+kVQtJVVo3X4WBnW7fYF/HM27ENebuDQ0eZ1fRdfLgwKXJ2g9/u8brfzwzuDqT1SE/n7m4goVTCYSwDSIAsTbTC3Noq5GdEMr3NDcPNybQuEeHtjxka0um+CreXrqysZUa9JARQ71a6WGXA/2nmal71tbSK6WUsPo+INz1zY1X9bb+hxNEO5HhzdEa9d3jNg2YjODZHuEfjb6W0jf0EyZNY0XFuNV/X4T6vRf+xU7Dpsz5xkp2mby5dVSP/vsEoIgTSPQBMbM+pG9ObMFZ1wv73GvqO+ubeM5ShVrNzB4faUuBjMJQCjLIxRAYxwbvo48mE1wedZ7Rvm6q8YwWvYYfq6wD5kdraEiNVrUzcACOxdFqur35sPoLKRvNO2mAx7jMRvm6xNRH/ip9UB9we2y/PfNprb07lJ5fxNdRiqVjTZ0RsHtkYDTUVOAHjrqt7Y8PSoiF+LjEkp/XNo9Y7nZnWq6T6v8NBx/LCs0MldA2BPu5PCQ8cD7uv92qYJ4WqfOb3fEzepFY2ZmaNUweH2lMjCfuMLId4TQuwRQqzULKsrhJgshFiv/F9HWS6EEC8LITYIIZYLIXpqnnONsv56IcQ1zvw47rOjCIpXSt15FVYzMzcNah1wf0sU/ZqCt9SibrWQx1rlmZetf+K8Lv7bdhWNeGtGYCU6tZhAIlCbTv/57d+ifg3tEEItszlzdtpdZF8gGomTm/l+7vqaaobqULVgQgg8cX4X1MhOR/XM0Lmcdg8j8+hl/1jJISo/LNuB9+cUADA+njcaBNBOveV2F5YqPBgUzCk/Z7M6OZplQIWLwVxGPCdcA2it/K1gLEepws1pLxQ/+46W4NWp65OueqmVy3cfABgZtGwMgClSynYApij3AeAsAO2UfzcBeAPwBX8AHgHQF0AfAI+oAWCyG/riDMzduC+m1zh4rEw/M2fxy2PMyJNi2j4QmlXTO5EKd34wtGND/+2zX5kd8z7pScRfr1gCohsGtNJdHm7OXK+Wsf/6PPZjaBXMSP5cvX5Fz/ArGbh3hO+YfeTszv5lZi0GrurXEiseHaH72GvT7M3W6r0HPy7fYes2qoptmrlqRsezUXA19Y89ustjle4R6NOqrm2vpw3S7hnRAWkegdev6Imvbj7Nv9wjhCuBjTrMUu+ChJtGdW0MgJk5Sh0M5qqGu79Yhud/Wafb5iaRhQ3mpJQzAQRXwzgPwIfK7Q8BnK9Z/pH0+R1AbSFEYwAjAEyWUh6QUh4EMBmhAWLS+OWuQQH3l247FNPrpXkE9ugM1bPaAFbvD3ekVxWsZIHCXdFuVCvb9PFYNK3tu8o9f3NqNXHu2swoM2fcmgAAXru8p+58o0ioGZNoBWcV88eMt/xc9UerVa2yOmUsPbL2RNDrLhxtDz0Vi6BE5/lf1vlvR5ppdqoIU7pHoJvB7100tN+1tw7xzbcc1bVxwPdhmke42jQ83SPwr4u6hlnbOeqfCsZylCoYzFUNx0p9c4ydbnlkt2jPBhtKKdXa6rsAqCmZpgC2adbbriwzWp6U2jesEX6lCKQJETCsUTV7g/WM38x7hgTcn752b0z7VLAvdC5VPL/M+rd1tvx8vDQwaJocrgBKo1rZWPLQcP99u3oeRnI1qn4MDZ+1J3kX92oGIHBYmhGjt+TzBdv0H4hC/RpZeOnP3fDk+V3Cr0yWmV0LOrW1e7/f6WkenNvNvj8/Vv7oC+FOzzX1gkiax+NqS4RgJcqbwswcpYo4J7vJZak4zNKU9P3Etv3UQoibhBALhRAL9+6NLSBxy7MTrTdN1uOVEulpsX1TaDMcAHDdB6HNc1VfLNiG05+bFnClOPgkeZNOMBfPJuZmv1fHSsuRP2Y8fl1tX7Nyt+hlgXYdPuGbRxnm7dYWORj64gxb9md70PwfM9kZ0VdAlZqvDHXIW4aFij9GcyZfmLxOd7me6pnh9/uCHs1C2iDc980Ky9ugUGYXJ8acpT9U/PoPFqDc5kukGWkCHTW94MpifP3S8vAVdnMy0nC0pCzserHyajJzbl57C846ztvky6pGM3+bKBG5MYed4i+eF8FiEW0wt1sZPgnlf3VyQyEAbZOmZsoyo+UhpJRvSyl7Syl7169fP8rdc96XN59qy+tIKVHulUiLtnSlItdik+91u4/g3q+Xo2D/MTyuqSxoVgZc/Q6LazCnub0kKHv0w1LffKYbHWg4DPjm/Uxb69z8nWD3fr3csF2FViyfh1EVyw4RZp0v6BGY4bjynXmWnqcNzt+Y7pvz9pNBI3Wtq0/LN3zMqBqmVuNa2f75POHUC+oXNm7+Vtw+boml51Ios8PZ6FrN1DV7MHO9vRf10jwC6ZqLKN8vjW0+ZIWFS5k5mWm2NSg3U5mZEzFlziMVXNxFnXowJ4LRJUSJLCPGC+5EToo2gvgBgFqR8hoA32uWX61UtewH4LAyHHMSgOFCiDpK4ZPhyrKk1bFxzfArWaD23doaY4l4vRP73zaGlovXltH/Y2eR//aPy8Kf0FgZZmlnYQGtxpr5J9e+H5h1dLrB+cBnp+G69xeYznmJpNectldeuk5mbua6vajwSjj5t2OnQfDetkFkLSnuGxWYUbE6NFg7/ErNAs9cF/6kvWntHBSMHY2nLggdAjkvzHzKCq/EsdIK3ffcqh+W7UBxSTl2F51AiYWMDFUy+/4wa4VSeMj6fMh0j0CPFrXDrBP4+f/zy2WWX1+PXqPuYFnpHscr8e47WoKpa3yjE9I8AoPa+S6GujGEdVnQvHG1N+RbMzfprE2UfDhnrmpJrkGW1loTjAPwG4AOQojtQogbAIwFMEwIsR7AUOU+AEwAsAnABgD/BfA3AJBSHgDwBIAFyr/HlWVJKzgTFu34WjXj812MV4f1vDt7c8gy7RVUbYGVGtkZIesGs5IJeuHibv7bdjb97dCoMmN0+HjgcKVvFjvfjwoAXphsPJw2kjlrakNdMxVe6WhFui8W6s8xi3i4r85h/9Wi7WGf1qZ+5cl7uJYXeq7o2zJk2V8/XmT6nGEvzcDh42X+Cc5WXNSzWciyzo9MQt+np+BOFkWJiNnJULXMdHx9y2m6j039w/rw6eZ1qyEzTLAe65D2YGUWUnOZ6R6UOjyj/vL//o6Dx3zfjelpAh6PQLfmtZHhQs+5zUHD8uvnupcVpMSw/2gJrnp3Hg4Uhx8hQZSwkjRmt1LN8jIpZWMpZYaUspmU8l0p5X4p5ZlSynZSyqFqYKZUsbxVStlGStlVSrlQ8zrvSSnbKv/ed/KHioeR/55l+2v2DHOFOZxfdU6Cfl65y3+7TBPMWTnBsTKZXRuAvD8nNJiM1pETgSfgdvWxUx0sLg05IQn22rSNhtu1OswVAFbtKAq7jldK3SGYdjEKgNMivPqYl5uFIR0Ch0MbZTq0mc3mmoI/9apnRrRNlV57BrOLKpv2+j7fSIbVta5vHGhOXLXL8DEKtX6PeT9Ao3Yb09buxQKL2fcKr0ST2uaFdCKZF2qFlYzbysIiTF3jzFBtANh5+HhAn0b1uyNNuNMjMXgLMc4aoCT0/pwCzFq/D//7fUu8d8UZmj+N45fvxCY2EU9NyZaSU/Ar1yZrbWxc3Fs5qTm1TWTDY544r3PIsuCiINpiApeeUjmNMTu9sijEbUPaYtkjlZUS1SttS7YeCrsPFZqr1DPXxzZf4r5vlvtva4eEAvb3sTvzxRkY8vz0sOst235Id/lLv1ovwPGlQVZMyzePMrLAyo4A95slkWU5PR6B96/rY2ndQ8f1C0AMbh/d3Fi9TE6r+yaEDcojUdck0EyyYldxV2xhKPK//9xdd/nFb/5maRvlFd6wvzdWW75YFUshILuc+szUgPvqHOw0j7D959Xz8W+BJ/AsFlH1qEWtUvWT1/5ct366GGe8YE/RMSI7MJiLQdOgK8DHSyOfQ9O8ji9D8eDojiGPRVoU5apT8/Hh9YEn1lsPBFYTW1BQWTykdk7l0MosTc+ymwa3Ri3NY5EMm2het/I9CQ7AIjVufmXQU9PCMNBYqD+j9oRTSomXp6wPWE+bLdO2FRi/PHzxDtWvFpohl5Z7LY3Rv7JfC//tV6duiFs53bEXBva00htK+vD3K3WfO6xzQ93l0Rry/PSQobjRurBn0nZQibvgY9HKCf75PZpi8zOj8Olf+ka1zTKvdL1QQaTl9/PHjMdr0zY4tDc+/sycR4QUJ3HCiqALSZxfVPWoh1mqfvS8QFFFJOnHzGAuBh9ef0rA/Wgqd6knAm3q5+KyPr4T84NKVb5Ih7wBwCn5gUOVtBUrgcC5DQWastFZmnkVwduN5MquU194DWtm4d6RHRx5ba3Oj1TW5dm8rxgvBpW81wbYTp4iHT5eZqms9+ntG/hvT1y1C5e8FT6D8e0S4zlttylNjyN1XvfAoKf3k7/i8wVbA5b9ZBDwBme/yi0UlFB9fYt+VdltB47hs/lbQwozRCorPQ2vXd4zpteoqoK/Nqx+MwghcFqbvJDlVgrOlFd4QwqcBAtuORGrcivlLBXqfM3nJsXWziacNE0wF485TOzJVfWovwVOBz0VXmlp3rndSspY8IoSF4O5GLRtEFjG/bGfVkX8GuoJj8cj8MyFXVEwdjQaKZUboym6Vy3T+twt7clRlmaYZfBV1UQYTtajRZ2QTOh5r84OGboVTWbqk3mhY/x/XLZDt/H6kRNl6PnEZLz4y1rsPeLsHxQrlSGHdgrMamkzr0aM5ssVjB2Nf46ILmDO0end9n9fW+vL1qBGdsB9vbmeRnq1rIsZ95wesvzsV2ZjzDcrcN5rcwAEVhCN1OiTjVsZrN1l3/DqVBN8EchoTpxVI/89C//4fKnpiVxZRfienS9f1iOm/QimXnywcg776lRnM3IqdZ8OFpe5MmcumFG1XEpdZeXWfw9i8eaMjej95K/ObkSHXiGhWev3xvS3hRJXIpz3RoLBnI22HYh8Yr0afOhdyYy2muHEOwcG3FczhsFZCu3BWk1zMh4856RGtvUAEQBevdzekyUA6NK0VshJ/7Lth/HB3IKAZcHDSsP5ecVOPPBt6PC/v49bEpLVBIDHflyNA8WleFnnpCxeQxwjNSvGuYxOK4mwH1fLeuGrYarFTwBgZOfIMzMrHxuhu3zEv2fG5WQ5GQQHc63rR9b2ItjmfcX4ZkkhnptonNU6WlKO8gqJr24+FQPahmb3ACBDuUr23a39/cue+fkP/LBsB/ZHccW/XNOoO5zXlZ6KACKqqhop9bsyr0YWsuIwp89Kz0dKLe8o1bOdbrpspYWNE/R+qqvenY/zlQuGlBqSdVABg7kYLX5oWEzP92fmNJezysp9C60UHNETPOxHnfe17WBgoKMNPm4c2Np/OzPoCtSWCAOkPE1Z6tUWKjdapVcQJni40pszIutrdMsni0OWRdIzLpbnRVIB0ynRFh+JVCQ9tjbYXCVsd9GJgGP/WBRXUnOz0vHWVb10H3t7Fntp6Yl1rtYnN+rPm/t2SSEmrtypG0QL4ctK9c6vizuHtjN9fe0f7bdmbMLt45bgxo8WGq5vpMIfzEX253TEv2dGvC2rbh3SBgBQLSPNUh+8WAVfAFxoYYQAUTTiNR9TW0dAaw1HZ1ACYDAXo+D5Pnd+tiSi56snAtrvpyNKUFAYZQnt+jUCe/zM23wAb0zfiFemGA/xMRsCpfZtiuY7dNTLkbVsiLXy2rj5W8OvFEaXR6z3sx9zVmXT7K6P/hLRdv510ckAgPevOwX3jOiA8bcPiOj5RiKp5uh0MJc/Zjx+XLYjonk7vWMcjhes79NTAnrQXXNqaI86K0YYZPTG/rwGN4fpcVcVaX+X+7WuG/Hz+xtk1korvLj5f4vxnk7rkzQh/N/J4b6vTm5WK2RZNBfQypQKwZH2r9t24DjW2VAFuVynf5063H7iql0BLQuc8vzFJwfcz4hmjgClhGiy25GIVx0Sjr+oWmSSfeL8xrXZd0t3YOa6vXj0h1XYZiGjpWbHtEVHcrN8w2Ku0FQqjETDmtm4rn9+wLJ/TVwT0j4h+FD9+pbT8OaVodmH2tV8V6Su7mftJDh4bpveyYaRSIpfJIJIMk4AMEUzH0wtVjOkQwPcOqQtOjcJPbm0Qr0Kr5oUQf+zETYXg9Dz93FLAobD/uuirsYrw3f8RmraP0+3vO6ZHe2tngn4Tpq37LevJUIq0GbOhnRoYLJmdJ4c/wfGL9+Jw8cqK5d6pbQ8zMuuQg0VEQyzDDb8pdizc8E9OIHQTJmV4jG2StaxShQzdbilW/LHjHdlO0kyi4KqKAZzNrh7WPuA+1e/Nx8fzC3AbePCZ+m0BVCCRVPNUvXw2Z3CrvOKMu9Lbfrcq2UdjNQ5ua8sOWxtf7QNoQHg1QjKcIf7wpx3/5lhX6PohLWy9EcsrmcmuNpluAzUDR9qhnHZdMJTOycwOzxDp3CL7vOqZaCOEqjfNbR9mLXDq5WTYXjV9M0ZlXOF9DIuLetVHjNXWrxooNUqL/y8OacNfm46CmzscZesjpWW47VpG1CiudBx06DWJs+I3q2fLsbtmtEQXgkcj7EgQf6Y8YaN7/Woc+YibSXjpOChaB0enOjo9rTX62pkpQe02tl52N4m7UTxwWiuKnF67qfdEuevTxK77Qz9cu5lFrI2FToFUNSKhIu2RD/vIJKrztPCnPwv3urbj6lrwvdH0/POLOtX6nbpVEG7WjMszkrW5tHvrVUV/WRe7EMyg/V8YrLlTKRdY/8v0TR/B4DfNu3HrPX6n+maXZVzGO8Z0QHVMtOx8elRuP3M6FoSaC15aBg2PjUq7Hp6AfvEOwb5b0fbhPn1K5xvIbDp6VH4u8HvOwCc/vx0vK8z/K8qeWnyOjw3aS36PTMFADC8U8Oos2AX9WwWdp0Z6/biaEm5/wr9uzZkBr5atN1yNkv9fY8mMwf4LgCpPUqjKaKkN1c3XON0u6nB7893DMTv95+JxrUqR2cUHXeu0AuRW1jnqmrhMMsqyOhEZfXOIvQfO9X0uWqfOb3XmB6nqk3B1MnskVaKVEVSGESvpPXorsal4fV8s0S/9H6w6hEUIDnzJOvDxNo+8LOl9eoFzbcEgGf/dLLOmuZq6PwcV707X3fdtzUFYtRG7GkeYcuQM49HwOMRGNox8iF1eq0NIjWqa2Osf+os03VeuLhbTNvweIThXC7VYz+udrRSYbwUnSjDrsMnkD9mPK56dx5OlFXgp+U7APgCktembUD+mPH4b9DFm7kb90e9zacu6GJpvUjmuWqZZQwf/SG0mq2eysxcdL9DPZ+YjI4PT8T3SwvRf+xUvBdBMLp8+yEMfHZayHK9UR2TV+/Gdxa/G6NVu1pGyPcqe86RnfT+VLlRSdpoE8HTSii5JWtveAZzDis8dBzrTSa5682ZU8Xax2yJQaXNC3s21V1u5CFlyKZ26Ew4wYHKfd9Y6zlWTeekvm/rwCqWf+oV/mq9lS/3pZpiBzcOaGW67n+v7h329fRsO3AMz09aq7s/egGUtihDpsUiAkYtLJ75+Y+QZRM18+nONumfFovL+pjP9VTnYDohI82D03SqnqqimY8XrF/revjlrkGm6zz2w2qcKKtwf66Sg05+9Bd/tm3W+n14ZsIfuO3TJXhx8jr0fGKyYSPsaKvDAr4s7fBO9s9xVI0ZeZLhY+Pmb8W3S7aHfQ3/nDmTAiiT7jQ/XgDgjs+WYsfhE7otUYzM33xAd7neiM+/fLQQd36+1NETX73RBk43kSZyYz6b0e/NRRGeTxE5gcGcC4a9NNNw6J1a78OJcrt1dDI/QOTFCEaf3BgFY0cHtC8IJzhQGDd/q7/fnZkflu0IuK83zOrJ87vgwdEd8bfT24Q8piq3MCbi68WVJ2oPnt0Jz16knxXLy800DJj+/efuusvzx4zHoWOl+OvHi/DqtA3YaLHcvjaojyToXvPEyJBlb83YFPIH6FhpZXARr5OsGtn6wdzXt5yKcX/pF/PrX3NavuFjdk1rat+wBu4407j0/ecLt+Gkhyaiw4MT8em8rUldHOX3Tft1f3c//G0LgMrWJ065e3h0jeytCNfL867Pw8+dU1vBmA2z7NCoRkT7tbLwMA4Ul4ZtSLxdU/FY2w/ULEs49uc1Ee1LONo5cXoZdreHfFLV48aAOHUbu4tCRw9RCkquUZYM5uxyfX/zzM7h42WYtnZPwFXq/UdL/H2N3DyvHtgucJjYA6OsZ9ys6tEitLz8le/OC/u84HmCzeqEDmHIzkjDjQNb416Tq+raJtFWBc89A4DOTWrit/t8RVeCS+af1qYezu/RFC/9WX/o3gWvz8Xqnb45albH22tXq2nQ10aP0TyzZyetxdQ1vgqa09dGN+cxUtFeJe3Vsq5uL8FIjejcyLAgSixFhYLdPNj4YoLW/d+uwODnptu2XTcdOVGGS9/+HVe8E/531ykdGtXA/27Q7zlnh/evO8X08Tb3T8CiLfoZMKCyAq/Vcvxf3nxq2HXOfmU2ej4xGRe9Mdd0PW3mV1vV0uw4f2umvX0RBz873X87I4GKwKSqkvIKrNpxON67kVC8Lg6z7Pv0lMDljm+Z3JRshU9U/Oa1ycPnmFeP7PXkr7ju/QXo8sgklFV4IaVEryd/9T/uZjBXu1pgxi64+qQdzu8RmlWSErpXmo+cKMOmvUfxf18tx9JthwIei/aq7oh/zzQdTmT0mHZI15X9WuCj6/v4T9I6Nq4JAPjPpd0BAAOUoNio1L2235tXShQeCl/VTdubS28unJl7RoRmMN6YvhHXf7AQT/60Gte+vyCi14tWz5Z1DLMUehlEJ/z49wGYr1P5NNJeYGZyMtOQmZ6aX6FSSkgpI+6dqKeODcNqB7TLQ8HY0TG/jp5wIxUqvBIXvfGb4eMrC30XbKweW5F8p63aUYS+T/+K75cWYpNOdn/c/G0B99UhzOo2bggzfNwOpZpRJ3o/mxsn2lXJA9+uxOiXZ2NPFc0Q6Z1suzLMkmFblZCsn3NqnonEyc93DLS03t1fLMPzvwTOL9Fr9GzH3CK17YBqxaPDQ9YZ0dm5OSnBflq+M2TZxW/+hjNemIHPF24Leaw4TCEJtaS+XtGNL3ReT6VtG/GQpo3DXUqbiXtGdMCT53dFvdysgPU+v6kfzuveFNP+eTpuHuTLzNQ0GDaoNfLfs8IWwwECT3yGRjhX6NYhbQ2bv7vZ+6du9UxseHqU7jzDaCtVRio3Kx0NamZj8UPDAuZh2j2ceW0EwamVYcZ69h4pwbYDx9DxoYkBfQqd1Oq+CWh13wRbXsutzzzYqh1FIcs+u0l/GO+yR0K/F4Pljxmv29NKvUhz9zBrw0E7KReFrNpdVII7PluKM16YgT92FmHiyl1hjwN1CLVRcYbVOu+NHbTBXLfmtQGwP5fdvlrkmx5QpNNfsCpYqZOVdOUEnMcxJTAGczbq2LimpT5oPyzbgdembQxYpleY4ekLzJsrW3HL6ZVl1Pvk1w2Zr2RXJUOr9Po3rdllXCCmbf1c09dT55Vd3Ls5Lg4qjPJ/X68wLEAxXhNUaguqdGxcE1PuHoxbdIbQZaZ7/MVYWuVVDzvfxszYC/U/2w4NK+fW5EaYmQOAvq3qWlovmoqTkRrmYOEKq+pWz8Tqx0fi3Wt8gWWbBubHU6SEECgYOxrf39o/7LpXvDPPP+TVqk/nbcUpT/2Kgc9Ow/GyCtzw4UKMm78Vg56dhvYWqqZ+vmArvlhgfFHDDbUiGC5sB7Xvp94oAG2BIa1I9vGrRdtx9iuz8M3iwOIo4YZF92xRG0Bswe1Z/5mFm/+3CDd8uBC/BVUJbd8wFy2UURbqMEuj433Uy7Oi3gcz2q9EtaiUG5UGq6bEeV/d/IyP6ASxbmzeaKqElRE3lDw4zJIA6Jebt0I7vO7XfwzGdf3zcZZOA+9I9WlVF3PHnAEAGDOqco7ZH4+PxG1D2uKPx90Z9qa1bNshbLPY5iBc5crmdauhYOxojOjcCI+d1znk8ek6PfSKg6rr5QSdXLWpnxtToGbFKINKkumaeTcVUTS2Cf5ZjGSlu5MteUeTnTM6kXbDmR0bomDsaEtZ1Gh0a14bt5v0n1Nd/8HCsOto3f9taBXY+75Zga0HjqG0woubP16Egn3FGPbiDPRWhm3P2bAPT/y0Gv/8chn+7+sVuPfr5XhnlrV5Ui/8slY3+xSLT20oaqP6+pZTMf72AVjwwFB8YDDX7SyllUmDGqEXyKplGl8gWfTgUH8wZOafXy7DysIi/OOLZThQXOpfHu6a2Gc3nYpVj40AAPzVhibql/3394D71/dvhfeuPQWvXd4TtZRRHYPb19d7qmO0FwbVLHjihBypJZFiZLf2pcIro/q7aIfg7F9+Pd93Rf0aWXqrE7mKwZzNos1ytddkZNo2yMUj53S2LWPWpHYOCsaORk9NUZKczDT8c0QHR+f8fHDdKXjv2tChdue9NgcDn52Gv49bErZlQSTvgd6J2l8/XoSuj0xCj8d9c3/W7CoKaSpsx3vw1AVd8OT51npiAUA1C0GXlYqcwf5i8STRqNG93ZpqCth8fL1zRSwSwfDO1i6+LCgwLqahZSWLN3HVLpz+/HSs33MU+476Wplc8c48vDt7s384FgA8OT60TYWeV6ZusLSeVYPa10fdKC9w6enVsi46N6mF+jWycLrBXLe2OtmoSXcOCtuzrl5uFmbeOySi/en5xGT/7XDfVJnpHn8PtosstFeJ1EW9miEvNwujgy4UTbhdf/j/4q0HdZdHa0BQ/0X1q5tz5tzndjZUu7VT8vWH+tvBqIiXO60J4P+OBYDp9wxBtcw0eNlNPKWMVJIoLQ2KqCUqBnM2S/MITL17MJY9Mjzkj6oZO4oEJJrTOzTAGScZD7X7cdkOjJu/1fH9OFJSjoPHyvDi5HUY+e9ZeHHyOtu3cUXflriyX0v/HL5w0k0q3zVShtxmZ0T+65mdkWapwELHCOftREsdUjagbZ4/W5CqujStZZgt0rr4TeNiGlqRZvEA8xP016aZB2pG7VOs6to0NPP60fV9YnrNcF6/oifaNcjFR9f3waiujbDowaH+x7RX0js0qoEr+rZ0dF8OHSuzvG67Brm4bUhbnNutCdY8MRLnd28S07ZHd21sWE2zUxP93/ULXzevlBmp/90YeLFG/RryxnZYkQGzEMLt7JU2eHRyjqxRz0o35sxJALsOBxadSRMiqouulLjUi/t2Vr52A4M5B7Sun4taORl49bIelp+Tyo1Vf7xtQFTPC26hYIVZmwWn+2EBwMD2ke9zsCl3D8b/buiLZnWiqzI6+/+G4FKdNgvx0CqvOp6/uBtejuB3IZmd3qEB1jwxEp8bFNpQ5Y8Zjw/nFhg+fuhYqeFjZsxO0J+btBa/b9qPv3y0MKA3mGpBQfhMze0GvfUKxo4OKPF/Se9mmP7P08PvcIxGdW2Myf8YjEHt6+P1K3oFFCyK1sQ7B+KRMNWJ9USSgRRC4J8jOuDly3ogOyMN/760BxY8MDT8Ew28dkXPqJ434qWZ/sIuKwsjL3dvdpwK/zBLnuw6ITgbdenblReJ3IovdhedwHOT1uDQ8coLGbPWR1foyYoNe/T7tbrx80op8dePFwUsS0sTzMxRQmAw56BUDtAi0TWKuVLzHzgTH0fRW6qPxQIgTunZog5+uWuQaUPzK/u1MH2N6lnp/rYH0WhcKwcX906MYA7wzXu0c6hdosvOSEPf1vUwtGND5OUa/9yP/LAqZP6matR/nClQcenbv2Py6t24QSfrN2nVLtPnDu3YALcNaeuvkLvsYV8FyOpKtdC83CxsfmYUNj8zCs/+qRvyk2yYiuqkRjVxXZi+oXqaGFSOtKp+jSw8dm7ovF87zDIYPrp2d2XxqbNfmR22SXmwCSuMjxn1JPd4aWSvSdZoh68eLSnH75sO6D7mlAH/moq+T0/Ba9M2+ufravfHCUbDwN0YVnrt+wtCip0wM0eJgsGcwxrXCp2EH2yOUqCEKukVL7AinkU2VO0b1jBtaP7k+bFXKQ2nV8s6liqrknPeuaY3Fj44zHSd695fgD+/9Zt/+M7t45Ygf8x47DjsbA+p1TuLMGnVLuzXzAExqvz6ztW9senpUXjnmlOQme7B+9f1QcHY0ahVLQOvXt4DEzQtWYRwtzqukyKd+2NHzaRrTss3DLxioRaK+vqW00zXO+mhiaYnxuOX7wwYamZ0zADAl8qcTbvnYZKP9mPaGJSxciOY237QuIrjUZfbJrgdTn1586kAAI9HcE5oiknWj5PBnMPevSb8HBqjXkCp5OpTrc9X+edwa/PO9ER6IjnXwUD6K+ULP1702l0AwJtX9nJ5T6o2s8Ib8wsOYN7mA+j3zBQAvrYlbvnrx4vQ68lfIaXExr1HQxpQq4Z2amhY3fXsk5ugZb3EzsC1qR/d/kXacLt6FK1E9DSvWy2iKsOjulqvemzUh1LLqH9ZWYUXt366GPd+vdy/7LlJa3XXBSoDPaeyNFWdOnz1gzmbcd5rcwIei3eyyK1y/eqID7dPwNWq5ekegfKKJD37J1PJdk3Snr8+ZKhTk5ooGDsah4+XYU/RCTSslY2TH/3F/7he5bVU1L15bXz025aw653brQluO0N/Xo5VbRvkGo6tDxbr0CgzvfPromDsaJSWeyEEsLDgIE5tU8+x7VlxUc9m/mpN5I4r+rbEsm2H8MXC7abrDXp2munjE+8ciJH/tn/4pVlz8J/+Ht1810SgnuDpFWaxYmSXxigYO9pSq4ZVj42wtfBDTmYa5t9/JmpXy0T7B437CX56Y1+c1jb2ebpab83YiDuHtkfRiTJUy0zD0RPlqF0t05+BmLluL0rKK5CVnoZjJkMo1dYEzFw4Q31bH/1xdchjB4tLo+pTapfxy3daunAQq78Oao1nfl7jempOLTLmEQIVPL4pATCYc0mtnAx/Y9pJdw5CWYUX+46WYFA7d/sAxcsFPZpi7a4jeGumfr+raplp6Ni4pi2FMn76+wB8OLcANwxohbYmjZWdrrSnUqsjxSOQe+ScTli05SB+Wr4TrfOq44VLurm+DwTcNqRd2GBua5jeiyc1qolRXRuZzlOyW5coA6FUMrBdnr+owzMXdtVtp2JXVk6rgUFmXfXmlb1sD+QA4PXpG/H69I0hy4d1qqxM3OHBiRgUpoedesLLAhHOMIshbv9sCb79W3/Hth1uHmTNHHdOLdUKrm4X2VG3m54m4tb3jkiLwyzjoEOjGujStBZO79DA8ebUiUIIgbuHdzCc0zbn/84IO5/DquyMNPx1cBukp3lMe8i5ceUw3q7r3wp3DlUynVXjUEtILepFV5kUAG4b0hb3jOgAAHj9il7Y8NRZUVVbjJS6zWRl1zCZj2/oi/vO8s2BPbOjr1qp1sx77J/jpqU2GH9gVEdcrPSnS/MI1zPsk1cH9j2cuW6v/7ZeE/TKzJyz+1VVlZn0fFiy9ZCj29520PzC0x87ixzdvko9fXI7OZauycwxMUeJgMEcuSYz3YMfbhuAyXcNClhev0aWP2tpt78MNJ73Ui3TuX44iUTNGnRuwixLPGkzG5H454gOuHVIZZP39DQPruvfSiloYf+8zO7Na6Ng7OiAbVZ1Nw1qjT8eH4kGNbIDhlP+dt8ZMQXqVtw3qiMKxo7GXwa1xrlKP7r/RVHpV7X0YfOiPNG4tn9+yDL1RJuZC2f8vmk/ymLsDRmJbxZvR/6Y8Th0rBSfzjPvD7tie+RtLqIh4jSUV70ILwQ4zJISAoM5cl27hjX8twvGjsaCB4Y6lqG8oEdTAMCzfzo5YNmmp0elTNW9cBrXysHXt5yKZy86OfzK5JinL4i8iunKx0aYPt6rZV1/ZbVgebmZ+M+l3VE7wmbt393q3PAsN9l5jiWEQI7m4s/s/xuCx8/rjMa13C1eNbBdfax8bERMQ7ZrV7O/TUi6J/RUgnPm7KcdsvrsxLUoOq7fqL66Axcq//HFMgDAzf9bhI17fXPSzz65ccA66jy9wR3snz5SdKLyZ1375Eise/Isf/bd7SOsRrbv50wTwpW2CEThcM4cxcWtQ9ogM835zFjbBjVQMHY0AKBH89rIzU53/QQsEfRqGd/+e+TLQF/VryU+/n0LGtbMwu6iEtP1h3dqaKmIQe+WdfB/I0/CJb2bYeCz01C/RhYm3jHIH3yc170pSsor0OHBibb8HMnGiYs2zepUw9Wn5tv+ulbYUdjivO5N8P1S+yqnZmeEBnPq285qlvYZv2JnwP3lQY3enzi/Cx76biWKHeztp+1nd063JmhaJwdvzfDNha+Vk4GjJeUYN38bnrnQ3ouH63ZV9kTMSvd9t6m/2W7HU+r2PULAZLQrJSG351/ahcEcxcU9I4z7sDlFmxEkigd1Dufdwzvg3q+Wm6779tW9Lb2mEAK3KE3qVxuUtM9QMicX92qG289sh4FhKmdSavvPpT3w9AVdkZnugUcItLnfuKKpFWk6IyvWKCffZv3IKDJ/H7ck4P4j36/y387LzcK5JzfBQ9+tdG1/jpWWo5mmIrSasXKC7ggD5YpBvE7AhWDmOVUl27gtDrMkInLJXcPa4/Yz2uLCHk2x5KFh6G1QhGd4lPPrjHg8AsseGY5nLuyK5nWr4ZMb++Kins1C1pty92Bbt0uJq3pWOjLSPEjziJj7barV/bQa1MiK6TUpPG0F3IfO7ogsnQypk3q3rBtQaSjLxvYcwb5ZXAjAN6dX5d9ynOIpjxBxLfAjpcTKwsOsGEvMzBERuSU3Kx3/GO6rElmneia+CqrgqvY0s5qVi4S2yFD/tnno3zYPXy+ubJew6elRKVVdlxfMrYu136ZeMPfw2Z1xzquzcU63JjG9NoXXp1VdnNe9qevzt4SoLHQDAJlpznx/HCst97fLuElTOTVec+ZUHg/iOmfuy4Xbce/Xy/HuNb1xZkd7LwBScmEwR0SUIOpWz8QlvZu7tr2CsaMxbc0e7Co6kVKBnFZq/lT2q1c9EzmZaaiemY61u4+Ef4Ji3F/66S5vWMuXmevbivN17VBuUrnypT93B+DM/FAzTWvnQGh+w/SG28bK65Xo9PAk/33tcEu1yI4b8VRmugel5YGfgS8zF79gTv093bS3GGd2jNtuUAJgMEdElCAWP2R/2fhwhpzUwPVtUuJZ+OBQCCFwoqwCJeVe1MrJQFmFF0dOlKNOtQzsPVqCPk9N8a8/6c5BaN8w1zCAYDVLe83duN/wsWo6wxvLK7xI18mY2kkIEZCZu7BHs4ACKXZ4bdqGgPvayqnqpp08xjLTPTivWxMM79wIf/loYcBjQghUJMDhXUUKc5MJzpkjIiKq4tSgLDsjzT8kNyPNg7rVMyGEQIMa2f7KwADQoVEN00xQmhrMcT6PLbQ95QrGjkaLupX9DatlhQZzPyyzr1qpGY/mGOipmQO8aMtBW17/hcnrAu4fL6us1OnGMEspJerXyNItwOIR8R1mSaRiMEdERESWfH5TP4y9MHzPRPUkPxEyF6kgODOnVo4UAsjUycCpfeHsYBqQa+L5tg1y/bcvemOubds32hd1iKeTAZVX+t7jHJ3sZ5pLwyx3Hj6OT+ZtMXyc8SQxmCMiopTD8xtn9G1dD5f2aRF2PXU0HDNz9nh39uaA+2owJ6Xzc+VKdebrqYVtPC6P8dNm5tRAUg1m3pi+Eds0FT7tIKWERwh0blITADCgbZ7/Mbf6zF33/gI88O1K7Dsa2JtUHeLKocz2Sda3ksEcERGlLs4niQu1GIZXSmw7cAxfLdoecjIaT2UVXhwvrcD+BNonK1rlVQcA1MzW6bsG+HtOAvZlrHYePgEAyNBUq2xSOxsAcOREmS3b0FOhcyGgfcPK7J/2V/vGDxfiXxPX2N5D0yt92xFCYN2TZ+Gj6/tUbt+lPnN7j/iO0eBNefy/Y47vQpWh/s4kW0EwFkAhIiIiW1UOs5QBJ9hNa+fgw+v7BAzJc5OUEo//tBrvzykIWP7a5T3RvmEu2jWsYfhcr1fCq2Rq4nWyd99ZJwEAahgEczcPboM3lDL+n8zbiiv7tYx5m+PmbwXga61y8JgveFOHdh4rrQhY96ZBrfH2zE0AgKMl5cjNiv40s2B/ceXtsaNxvLQCOZmVwx3VCwYVXolf/9jtXy6ltCVbqQaT+4tLAfiKoWh5hPCvc7SkHF4pDYPsWKjbD26Orv6O/WviGvxr4hp8cmNf9NdkDgFg1+ETaFgzy/VKp8lKDYzdzjjHipk5IiIispU/mAuaNFd46DiGvjgDo1+ehRNlFXpPdVTnRyaFBHIAcOunizHspZnIHzMe+WPG+4eHer0SU/7YjZcmr0Pr+yeg7QM/Y9TLs1zea2Bw+/oAgKFKPzG1R+RJjQKDT20/yQe/W2nLtvcpmSFtQKAGMc9NWhuw7j+VPpqAeTsFK3YXnQi4rw3kgMpg7mhJecDy+79dacvw3ilKgPjJvK26j3s8lZm5Lo9MwsmP/hLzNs2oQzoXbTmIHYeO+4sMqa54Z17A/Q17jqDfM1PwzqzAIbpkTP08kyuUYzBHREQpiFXm4ks90Q6uRqhataMIJz000bSwg912HT4Rkkky0vr+CcgfMx6t75+AGz5ciP9MWe9/bM2uI/hpuTvVIlWt8qqjVk6GPyPYS6kcuWaX9Z6A0VqoVKZsXrca2tT3DfPcuPeo7rra7NX3S2N7j6av3Wv6uNqsXh2GqBo3fyte+lX/uIvEK1M3mD6+eMshLNxyEFv3V87Tc3KOaFmFF6XlXlz0xlycNnYq9JLDr05d779Ism637zN6asIf/D60SDIzR0RElFhE0l1jTQ1WRyE+8O1Kxyofakkp0e+ZKeFXtOi2T5dg1nrzYENrz5ET6Pv0r8gfMx7PTVoT0GrAitIKb8CctZGdGwHwNXt30p6iE9iqFBV59JxOaFnPF8xNWuXLWr15ZS8AwD0jOoQ895EfVsW07c/m62fEVOnKQbZVp+hJuEDMihWFh00fV4uxHCurzAy2vn+C/3ZZhReHjpXGvB+qCq/EXs0cT7248flf1uGkhyYif8x4jPl6uX95q/sm4OHv7cnUpjJ/Zi7JoqMk210iIiJKdJHM0Vm05aDtVQiDnfKUfYGc6qp35wcMyTSyoOAA+jw1BbuLfCfir03biHYP/Izl2w+F3caB4lJ8t6QQn87bin1HKwODzcp8sqZ1ckKec1HPZv7bU9fsDnk8Ele+Wzl0r0HNbExds8e33dq+7Y7s0ggFY0fj1iFtdZ8fS0ZIraI5pEN93cfVzJxR0GhXNurCnk1NH99/NDBgm7x6Nz6dtxV//3QJuj8+GWt2FdmSsSv3ejFx5S7//XC/YkUnAoeffvSbe1nwZKUeMsl2CZDBHBERETnqifO74Ifb+hs+PvDZaXhvtjNze46XVoRU0lz44FAseWgYlj86HLPuHRLT67e+fwL+2FmEzfuKQ+Z5AcDFb/6m+7xzX52ju3zmur1Yvv0QPpu/FT2fmIw7P18ass6nyjyu5dtDs0cvXNLNf/v6DxbG9L6qQ/UAXwB3xkkNAADdW9Q2fM71/Vv5b//lo4VRb/s65XWeukC/r2FamPRvmU1NDh87t7Pp48Fz1f7y0ULc/+0KTFzlC7xG/nsWznl1dsyVPzfuLcYxzfxA7fxIq6LJClclapEZDrMkIiIi0riqX0uc3Kw2CsaOxuiTG+uu8/hPq7F1/zFs2BPZPLBnJvyB6z9YgIUFB7Cg4ACklJi5bq8/MzPi3zMD1n/6gq7Iy81CneqZqJmdgeZ1q6Fg7Gh8fctp/n5ievq1rmv42Fn/mYUhz09H36crM4AnyiqQP2a86b7njxmP05+bhuOlFdi8rxj/+XU9rn5vPs59dQ7GfLPCyo8f1uM/rbbldQDgXKW/XGulRYKevw2pbI/w6x97og5i1MqQ1Q0qYmqrXQLAG1f0DLh/ZVCQFS2jyqGqdAtjilftKELXGAuk7D1S4q8manW7wdSsMOlL1mqWbE1ARERErnnt8p547XJfBurq9+YHPDbouco2BpPvGmTYKqDCK/HET6vxwdwC/zJ1CKD/tdrXx8x1gfPa8utVw+V99Zue92pZB+NvH+i/r1fi/rslhZi2do9pcY/8MeNx25C2eHWatXlbBfuPoePDEy2tq/rxtgE459XZltf3eqUt7RTO7dYER06U4ZJTmhuuk5ebFXB/zNcr8FpQoGVFhVK+MScjTffx4M/29A4N8MF1p+Da9xcAAOYrgX0sZfmv658fdp3yCIZQ3vjhQjSvm4OHRneK+POoVz0zoELpoz9WBun3juyAZyeu1Xuarj1HTqBBjeyItl8V+OfMJVcsx8wcERER2a9xLd/J4pX99IOnQe3ro2DsaMPnD3tpJoa9OAP5Y8Zj5+HjePGXtVi3+wgmr96NNvdPCAjk9ASf7APA9HusD6nUCwLO79EU/7m0Bx46u5Ppc60GctHKz6tm+vjkuwYF3H9vjj1DWD0egatOzUdWun6ApWf8ip1Rbauk3AshApuVa/VoUSfgfnaGB6d3aBCwbJFSiTNSpeW+QPJ/v9s7z+zXP3bj/TkFuG3c4oAhud8vLcSx0nKTZwK3fLLY+LHBbbDqsRH410WBQ1LH3z4Az150csj6fRyYQ5oKFm85BCD5grmYMnNCiAIARwBUACiXUvYWQtQF8DmAfAAFAC6RUh4Uvm/F/wAYBeAYgGullMZHJhERESWtuWPOgFeGn9tkZv0e35ytU5+ZCgB4OYYqhcseHh71c4PdMKAVLuzRFD2emGz5OaseG4HOj0wCAAxsl4dZ6/dFtM2f/j7Af1ttxn1xr2a66wZnNJ8c/wcu79sC1TKjO+17YFTHiNZ/+6peuOnjRf77+WPGY+nDw1C7mvXqm7uLTiDD4zHMrNUMmjOmrjfr3iH+RvV/evM30wsGRvYX++ZY2jXvLtiEFbswYcWukOUv/bkbLuhR+ZkWWRyiKoRA9ax0/PmUFvjzKS2wZX8xTpR50aFRDXRuUgvdmtcOGW788Pcr8eDoTiHN0Ksytfl8VRxmOURKqf1GGgNgipRyrBBijHL//wCcBaCd8q8vgDeU/4mIiCjFCCFgkFQJUDB2NCau3IWb/7co/MoxqFUt8oIRZupUz0TB2NEY/tKMgEIheprWzkH1rHRsfHoUBHwZrnDz6QDgjjPb4a5h7UOWCyHCBikX9myKbxYX+u93enhSVIENAPxlUOuI1h+utE7Q6v745Ii275WVFS31NKyRpbu8eV3zrKUValXDR88xz8BqLXloGJYXHsbeIyU4eqIM5V6JJ8f/EdF27/p8Ge76fFlEzzmtTb2QZWoLCVWHRqHDlT/6bQu+W1KI5Y+OiGh7kdi09yhys9MjGtIppcSHcwtwdrcmIUN23RLckD3RORGOnwfgQ+X2hwDO1yz/SPr8DqC2EEJ/FjQREVEM2CI3uXRpalx4xIo1T4zEnUPb2bQ3kZl05yA8ONo4c1UwdjTmjDkDgC9Lqc6V6ta8tuFzXrmsBwrGjtYN5Kx68ZLuIcuKS8yH8tlp0p2DQpYVHjpu6bkPf78SXy3abrpO73zjgjQD2+X5b28/GHnbC7XH3eqdRYbrnB1UyKdO9UwMbl8ff+rVDNf2b4Xr+rfCzHuGYM0TIyPefiQ+uK6PpfXev+6UkDYPwe0L7HbGCzMsD+lcUHAAD323ErPW78OjP67GHZ8tcXTfzNgxv9RNsQZzEsAvQohFQoiblGUNpZTqAOldABoqt5sC2KZ57nZlWQAhxE1CiIVCiIV791pvyElERBQsyS6wVlnN6lTD3DFn4J4RHfDdrf2RnWHt9KRGVjrm3X8msjPScOfQ9lj/1Fm664Wb4xYLIQRuGNAKX918KgrGjkYLJTP0wKiOGH/7AMPnvXVlL9w0qDU2PT0KBWNHo2DsaMy//0ysfXIkzlGqRtpNHeZpRcG+4vArmdDLBvUfOxX5Y8aH7QFnpSdarkGVSyAwwBnwr2moMClS4vVKnFAagKvmbT4AAFhYYDznLlzD9jSPQIt61ZCdkYZFDw7Fz3cMNF0/WlaHMQ/p0ADv6wR+fx+3JOIKslZoe0eqn/eB4lJ8rMxDlFJiZeFhzN2wD/ljxuPiN3/Dx79v8RdFmrNhP/47c5Pt+5WKYh1mOUBKWSiEaABgshBijfZBKaUUQkR0gVRK+TaAtwGgd+/evLhKRERUBTSpneNvPr3mibOwZOtBXPD6XMP1/zGsPW4/MzAbl5HmwfqnzsLeIyU4baxvnl20QwsjIYTwZ4om3jkQxSUVqG8wDFDVqFY27g+ai9agpr0VBgvGjrY0nFPPLp2eeZH6+Y6BOOs/s0KWt75/AjY/E/i5HC0px+odRbjtU2vlFMzmegUHODd8uAD/uuhkbNx7FK3yqqO4pBz1a2SjVk4G/j5uCcav2IkVjw5HblY6pFSasW9GSEEVrWGdGuFDJejcYHARQVUvNwv1crOw8MGhWFF4GNcpFTftEGkSKS83K6Dv4o/LduDHZTswsF0e9h4pwd+GtMXt45ZgWKeGuO+sk9C4Vg5yMq0VvNl3tAS9n/w1ZHmr+ybgtDb1sLvoBDbuLUaTWtlYtv0wXp6y3vT1nprwB56a8AeWPDQMdcIEz5E6WlKOHYeOo2Z2BhrVSu7KnjEFc1LKQuX/PUKIbwH0AbBbCNFYSrlTGUap1gouBKCtZdtMWUZEREQUoEeLOvjtvjNQp1om5m8+gPx61bFx31G0b1gDTWvnGD4vI82DJrVzsPThYS7ubaVqmelRFxpxwjMXdsV9mp51+WPG46+DW+O+s8yLmoSrrmhFx8Y1Q+buAb45afljxuPa0/Kxv7gUPy4zbvVgxiMqe4OZmb52b0APQD16feCqZxkHMdqehOlp1jLJeblZGNKhAQrGjsbfxy1Bzex0fKI0gI/Eyc1q+RvGR9p64Z1reuP810Ib1qsFeW4f5xveOHn1bkxevdv/+LWn5WNYp4ZoXb86GtfS//3TC+RUczfu99++4cPImsn3eCKy+Zaqt2duxOD2DdC4djbSPQLVMtPxzqxNmLFuL7YeOIYt+yuzh8HDZpNJ1N82QojqADxSyiPK7eEAHgfwA4BrAIxV/v9eecoPAG4TQnwGX+GTw5rhmERERPbhuI6UoJ40Dmrvm+vTop714haRVE5MZZee0jwgmAOAt2ZswlszNmHhg0ORl5uFVTsOo0PDGgFBiToSMtNioGLk+T91g0cI3Tlw4dpLhLPowWGGFUUv69Mc4+Zv033MqrYNcg0fq6VU0/xzb+Oee2ZeuawHAOCpC3ztBKSUuPuLZThRXoHeLevi2Ulr0K5BDfz49wEh2dUfbjMevhtO9+a1MfOeIQE9Ha34YG6B//OafNcgtKxXHSXlFVi9owj7i0vxN5PWCXYY+uIM/HjbAN0s4crCw/hwbgH6tKqLe75aHvDY0xPWhKyv56flyRuSxHLpqCGAb5UrAukAPpVSThRCLADwhRDiBgBbAFyirD8BvrYEG+BrTXBdDNsmIiIKi1PmqKoTQmDDU2eh7QM/hzzW+8lf8cT5XfCQ0ox68zOj/JmebKVZ91tX94pp+x6PwMPndApb0CQadapnIs0j8JeBodU2bzujXczBnFmRFY8nfEXRSAgh8OKfu/vvXz+glf/2u9f0xv7iUizZeghdm9aKeVvN6+bg6lNbWpqbqGfYSzPDr2SzDXuOouPDE/3v+YY9R3H3l8tQ4fViZaGvUM2XDhxjySDqYE5KuQlAN53l+wGcqbNcArg12u0RERERUeTMhgGqgRzgm9sUrK4NGc6a2RlY+dgIdImgAAvgmxcZzsanR+kub1o7B1/fcioueuO3iLaplZ4gVQ3P7OirJXhJlFnAYEIIPH5eFzx+Xpeo51SG07FxTYw56yR8/NsWf/82M5/e2BeXvzMPAHButyb4wWDorVP7m8zYKZCIiIgoxd2gyfREIstiZdFwcrPSMfXuwahjod9fq7zqeOzczrh5cJuYttmrpXFmzQqzipmpYlNQMHxd/3xbXnfC7QMwuH19vHNNb3x0vX77hMl3DcJNg1rjhYu74bS2le0kXr6sBzY/ox+kOy0/gqHciUKEKw8bT71795YLF0Y2SZKIiOjzBVvxf1+vwMW9muG5i0MGkRBVSUbVBs3MHXMGmpgUnIlGcHalY+OaeOea3mhcMxsvT12Pq0/NR12bqhceKC5FWYUXM9buxb1fV86n6pNfF9Wz0jBtrX4brIw0gfVPxSegSBTRZME8Atj0TOjwU/W1/nNpd5ySXxc5GWkhFSrX7T6C7PQ0/9zYw8fLcOsnizF7w74o9j46a58ciax0a9U73SSEWCSl7K33WOpfciAioiqLfeaIKuXlZuH0DvUx3SCA0ZOTYf+Jbc8WtdG4Vg4ePbczth44hl4t6/gfu3No9I3S9ahB4SWnNMclpzTHFwu3YUSnRqilZAiPl1bAKyWqK1m4Cq/EBa/Pialhe6qYO+YMZGekoW71TCwoOIB0jzBsF3Jam3ro1bIO7h7eQffxd6/pjdb1c9Eqr7rh9to3DOxNWCsnA/+7sW/UQytvHdIG/xjWAbuKTqC/0qoEAH79xyA0rJmNIyfKkZudjp2HTuCDuQV46vwuSdcwHGBmjoiIUpCambukdzM8+ydm5oiCvTR5Hf4Tps8XAKx/6ixkxFjRklLHtDV7UHjoOGau24vFWw/h4LFSrHlipKPHyNGScsP5lh/f0AcD29UP+xpqvBNpK4dEwcwcEREREfndNaw9Tsmviyvf9RWd+O7W/vh60XZ8/HtghUMGcqQ15CRfI/Ur+7V0bZu5WenY/MwoPDdpLdLTPMjO8OBvp7eN6DWSNYizgsEcERERURU0oF2evwCGxyPQvXltnNW1Ed6dtRlPX9gVDWtmx3kPiXyEELh35Enx3o2ExGCOiIhSTgLPICBKKMFzhE5rk4fT2uQZrE1EiYa5cyIiSlmCbcOJiCiFMZgjIiIiIiJKQgzmiIgoZUlwvCUREaUuBnNERERERERJiMEcERGlLM6ZIyKiVMZgjoiIiIiIKAkxmCMiIiIiIkpCDOaIiIiIiIiSEIM5IiJKOaxhSUREVQGDOSIiSlmC9U+IiCiFMZgjIiIiIiJKQgzmiIiIiIiIkhCDOSIiSjmSk+aIiKgKYDBHREQpi3PmiIgolTGYIyIiIiIiSkIM5oiIiIiIiJIQgzkiIko5kp3miIioCmAwR0REKYyT5oiIKHUxmCMiIiIiIkpCDOaIiIiIiIiSEIM5IiIiIiKiJMRgjoiIUg6bhhMRUVXAYI6IiIiIiCgJMZgjIqKUI1jEkoiIqgAGc0REREREREmIwRwREaUczpkjIqKqgMEcERGlLA63JCKiVMZgjoiIiIiIKAkxmCMiIiIiIkpCDOaIiCjlcMocERFVBQzmiIgoZXHKHBERpTIGc0REREREREmIwRwREREREVESYjBHRERERESUhBjMERFR6mHXcCIiqgIYzBERUcpi03AiIkplDOaIiIiIiIiSEIM5IiIiIiKiJMRgjoiIUg5nzBERUVXAYI6IiFKWYNtwIiJKYa4Hc0KIkUKItUKIDUKIMW5vn4iIiIiIKBW4GswJIdIAvAbgLACdAFwmhOjk5j4QERERERGlArczc30AbJBSbpJSlgL4DMB5Lu8DERGlOHVwJVsTEBFRKkt3eXtNAWzT3N8OoK92BSHETQBuAoAWLVq4t2dERJQyLu7dHKt3HsE/hrWP964QERE5JuEKoEgp35ZS9pZS9q5fv368d4eIiJJQdkYanrmwK2pXy4z3rhARETnG7WCuEEBzzf1myjIiIiIiIiKKgNvB3AIA7YQQrYQQmQAuBfCDy/tARERERESU9FydMyelLBdC3AZgEoA0AO9JKVe5uQ9ERERERESpwO0CKJBSTgAwwe3tEhERERERpZKEK4BCRERERERE4TGYIyIiIiIiSkIM5oiIiIiIiJIQgzkiIiIiIqIkxGCOiIiIiIgoCTGYIyIiIiIiSkIM5oiIiIiIiJIQgzkiIiIiIqIkxGCOiIiIiIgoCQkpZbz3wZAQYi+ALfHeDx15APbFeycoIfHYICM8NsgIjw0ywmODjPDYqFpaSinr6z2Q0MFcohJCLJRS9o73flDi4bFBRnhskBEeG2SExwYZ4bFBKg6zJCIiIiIiSkIM5oiIiIiIiJIQg7novB3vHaCExWODjPDYICM8NsgIjw0ywmODAHDOHBERERERUVJiZo6IiIiIiCgJpUwwJ4R4TwixRwixUrOsmxDiNyHECiHEj0KImsryK4QQSzX/vEKI7spjvZT1NwghXhZCCIPtjRRCrFXWG6NZfpuyTAoh8kz2t5UQYp6y7udCiExl+SAhxGIhRLkQ4k82vT1VWgodG9cKIfZq9u1Gm96iKiuFjo2WQogpQojlQojpQohmNr1FVVYSHhu66wkhzlOOi6VCiIVCiAE2vD1VVgodF/do9mulEKJCCFHXhreoykrCY+MT5fkrlX3PUJafpOxziRDinza9PeQkKWVK/AMwCEBPACs1yxYAGKzcvh7AEzrP6wpgo+b+fAD9AAgAPwM4S+c5aQA2AmgNIBPAMgCdlMd6AMgHUAAgz2R/vwBwqXL7TQC3KLfzAZwM4CMAf4r3+5oK/1Lo2LgWwKvxfj9T6V8KHRtfArhGuX0GgI/j/d4m+78kPDZ01wOQi8opFScDWBPv9zaZ/6XKcRG0zjkApsb7vU32f0l4bIxStiEAjEPl35MGAE4B8BSAf8b7feW/8P9SJjMnpZwJ4EDQ4vYAZiq3JwO4SOeplwH4DACEEI0B1JRS/i59R/RHAM7XeU4fABuklJuklKXK889T9mOJlLLAbF+VqyxnAPhKWfShuh0pZYGUcjkAr9lrkHWpcmyQ/VLo2OgEYKpye5r6uhS9ZDo2zNaTUh5Vtg0A1QFwonwMUuW40Nm3ceFei8wl4bExQSrgCyCbKcv3SCkXACgL9xqUGFImmDOwCpUnNRcDaK6zzp9R+SXWFMB2zWPblWXBmgLYZmE9I/UAHJJSlkf5fIpdsh4bFylDpr4SQujtM8UuGY+NZQAuVG5fAKCGEKJeBK9N1iTqsWFKCHGBEGINgPHwZQfIXkl5XACAEKIagJEAvrbzdckv4Y8NZXjlVQAmRvN8ir9UD+auB/A3IcQiADUAlGofFEL0BXBMSrlS78mU0pLx2PgRQL6U8mT4rvB9GOf9SVXJeGz8E8BgIcQSAIMBFAKoiO8upaRkPDYgpfxWSnkSfFf4n4jz7qSipDwuFOcAmCOlDM4okT2S4dh4HcBMKeWsOO4DxSA93jvgJCnlGgDDAUAI0R7A6KBVLkXg0IJCKGlmRTMAhUoG5Edl2ZvwXQVvHrye2b4IISYBaAhgIYC/AKgthEhXrrKHfT7ZKxmPDSnlfs3T3gHwrPlPSdFI0mNjB5TMnBAiF8BFUspDFn5cikCiHhtSSkvFkKSUM4UQrYUQeVLKfVaeQ+El+XERvG9ko0Q/NoQQjwCoD+Cv1n8qSjhOTcaLxz/4JnxqJ542UP73wDfu+HrNYx74DvzWQa8RPPF0lM520gFsAtAKlRNPOwetUwDziadfIrCQwd+CHv8ALIDCY0NzbABorFnnAgC/x/t9TYV/KXJs5AHwKLefAvB4vN/XVPiXTMeG0XoA2qKyAEpPZR9FvN/bZP6XCseFsqwWfHO8qsf7PU2Vf8l0bAC4EcBcADkGjz8KFkBJin9x3wHbfhDflY2d8E3Y3A7gBgB3AFin/Bur/QMG4HTonAwD6A1gJXxVgl41+qMHXxWgdcp6D2iW365svxzADgDvGDy/tfILuwG+E7QsZfkpyvOLAewHsCre722y/0uhY+MZ+MbfL4OvyMVJ8X5vk/1fCh0bfwKwXnntd9Tl/Feljg3d9QD8n/K9sRTAbwAGxPu9TeZ/qXJcKI9dC+CzeL+nqfIvCY+NcuW5S5V/DyvLGynPLwJwSLldM97vL/8Z/1Ov1hEREREREVESSfUCKERERERERCmJwRwREREREVESYjBHRERERESUhBjMERERERERJSEGc0REREREREmIwRwREREREVESYjBHRERERESUhBjMERERERERJaH/B7ciN8RmjikyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df5['CO2'].fillna(value=None, method='backfill', axis=None, limit=None, downcast=None,inplace=True)\n",
    "plt.figure(figsize = (15, 6))\n",
    "plt.plot(df5.index, df5['CO2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df6=df5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import array\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Flatten\n",
    "from keras.layers import ConvLSTM2D\n",
    "from keras.layers.normalization import BatchNormalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_sequence(sequence, n_steps):\n",
    "    X, y =list(),list()\n",
    "    for i in range(len(sequence)):\n",
    "        end_ix = i + n_steps\n",
    "        if end_ix >len(sequence)-1:\n",
    "            break\n",
    "        seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]\n",
    "        X.append(seq_x)\n",
    "        y.append(seq_y)\n",
    "    return array(X), array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_size = int(len(df6)*0.8)\n",
    "train_data = df6.iloc[:train_size]\n",
    "test_data = df6.iloc[train_size:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler().fit(train_data)\n",
    "train_scaled = scaler.transform(train_data)\n",
    "test_scaled = scaler.transform(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51964, 1)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_scaled.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12991, 1)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_scaled.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "col_train=train_scaled[:,0]\n",
    "# seq_train=col_train.values\n",
    "raw_seq_train=col_train.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51964"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(raw_seq_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "col_test=test_scaled[:,0]\n",
    "# seq_test=col_test.values\n",
    "raw_seq_test=col_test.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12991"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(raw_seq_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "n_steps = 30\n",
    "X_train, y_train = split_sequence(raw_seq_train, n_steps)\n",
    "X_test, y_test = split_sequence(raw_seq_test, n_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x28ec7ea83a0>]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 8))\n",
    "range_future = len(y_test)\n",
    "plt.plot(np.arange(range_future), np.array(y_test),  label='Test data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train=y_train.reshape(y_train.shape[0],1)\n",
    "y_test=y_test.reshape(y_test.shape[0],1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(51934, 30)\n",
      "(51934, 1)\n",
      "(12961, 30)\n",
      "(12961, 1)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "n_features = 1\n",
    "n_seq = 10\n",
    "n_steps = 3\n",
    "X_train = X_train.reshape((X_train.shape[0], n_seq, 1, n_steps, n_features))\n",
    "X_test= X_test.reshape((X_test.shape[0], n_seq, 1, n_steps, n_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(51934, 10, 1, 3, 1)\n",
      "(51934, 1)\n",
      "(12961, 10, 1, 3, 1)\n",
      "(12961, 1)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv_lst_m2d (ConvLSTM2D)    (None, 1, 2, 64)          33536     \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1)                 129       \n",
      "=================================================================\n",
      "Total params: 33,665\n",
      "Trainable params: 33,665\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(ConvLSTM2D(filters=64, kernel_size=(1,2), activation='relu', input_shape=(n_seq, 1, n_steps, n_features)))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(1))\n",
    "model.compile(optimizer='adam', loss='mse')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def fit_model(model):\n",
    "#     early_stop = keras.callbacks.EarlyStopping(monitor = 'val_loss',\n",
    "#                                                patience = 10)\n",
    "#     history = model.fit(X, y, epochs = 5, verbose=0)\n",
    "#                         #validation_split = 0.2,\n",
    "#                         #batch_size = 16, shuffle = False, \n",
    "#                         #callbacks = [early_stop])\n",
    "#     return history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def fit_model(model):\n",
    "    history = model.fit(X_train, y_train, epochs = 10, verbose=0,\n",
    "                     validation_split = 0.2,\n",
    "                     batch_size = 16)\n",
    "    return history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "history_convlstm=fit_model(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_loss (history, model_name):\n",
    "    plt.figure(figsize = (8, 5))\n",
    "    plt.plot(history.history['loss'])\n",
    "    plt.plot(history.history['val_loss'])\n",
    "    plt.title('Model Train vs Validation Loss for'+ model_name)\n",
    "    plt.ylabel('Loss')\n",
    "    plt.xlabel('epoch')\n",
    "    plt.legend(['Train loss', 'Validation loss'], loc='upper right')\n",
    " \n",
    "plot_loss (history_convlstm, 'ConvLSTM')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.2998998 ],\n",
       "       [0.2994095 ],\n",
       "       [0.29809362],\n",
       "       ...,\n",
       "       [0.33432144],\n",
       "       [0.293605  ],\n",
       "       [0.30435342]], dtype=float32)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def prediction(model):\n",
    "    prediction = model.predict(X_test)\n",
    "#     prediction = scaler.inverse_transform(prediction)\n",
    "    return prediction\n",
    "prediction= prediction(model)\n",
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12961"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2098278.],\n",
       "       [2098278.],\n",
       "       [2164428.],\n",
       "       ...,\n",
       "       [2222640.],\n",
       "       [2222640.],\n",
       "       [2116800.]])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_future(prediction, model_name, y_test):\n",
    "    plt.figure(figsize=(15, 8))\n",
    "    range_future = len(prediction)\n",
    "    plt.plot(np.arange(range_future), np.array(y_test),  label='Test data')\n",
    "    plt.plot(np.arange(range_future),np.array(prediction),label='Prediction')\n",
    "    plt.title('Test data vs prediction for ' + model_name)\n",
    "    plt.legend(loc='upper left',fontsize=15)\n",
    "    plt.xlabel('Time',fontsize=14)\n",
    "    plt.ylabel('CO2',fontsize=14)\n",
    "\n",
    "plot_future(prediction,'ConvLSTM', y_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ConvLSTM:\n",
      "Mean Square Error: 0.0000\n",
      "Root Mean Square Error: 0.0047\n",
      "Mean Absolute  Error: 0.0030\n"
     ]
    }
   ],
   "source": [
    "def evaluate_prediction(predictions, actual, model_name):\n",
    "    dif=predictions-actual\n",
    "    errors = np.nan_to_num(dif, nan=0)\n",
    "    mse = np.square(errors).mean()\n",
    "    rmse = np.sqrt(mse)\n",
    "    mae = np.abs(errors).mean()\n",
    "    print(model_name + ':')\n",
    "    print('Mean Square Error: {:.4f}'.format(mse))\n",
    "    print('Root Mean Square Error: {:.4f}'.format(rmse))\n",
    "    print('Mean Absolute  Error: {:.4f}'.format(mae))\n",
    "    return errors, mse, rmse, mae\n",
    "\n",
    "errors, mse, rmse, mae= evaluate_prediction(prediction, y_test, 'ConvLSTM')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Square Error: 0.0000\n",
      "Root Mean Square Error: 0.0047\n",
      "Mean Absolute  Error: 0.0030\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt\n",
    "mae = mean_absolute_error(y_test,prediction)\n",
    "mse = mean_squared_error(y_test,prediction)\n",
    "rmse=sqrt(mse)\n",
    "print('Mean Square Error: {:.4f}'.format(mse))\n",
    "print('Root Mean Square Error: {:.4f}'.format(rmse))\n",
    "print('Mean Absolute  Error: {:.4f}'.format(mae))\n",
    "# print('Test RMSE: ' % mse)\n",
    "# print('Mean Square Error: {}'.format(mse))\n",
    "# print(mse)\n",
    "# print(rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
